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• Guide to Experimental Design | Overview, Steps, & Examples

Guide to Experimental Design | Overview, 5 steps & Examples

Published on December 3, 2019 by Rebecca Bevans . Revised on June 21, 2023.

Experiments are used to study causal relationships . You manipulate one or more independent variables and measure their effect on one or more dependent variables.

Experimental design create a set of procedures to systematically test a hypothesis . A good experimental design requires a strong understanding of the system you are studying.

There are five key steps in designing an experiment:

• Consider your variables and how they are related
• Write a specific, testable hypothesis
• Design experimental treatments to manipulate your independent variable
• Assign subjects to groups, either between-subjects or within-subjects
• Plan how you will measure your dependent variable

For valid conclusions, you also need to select a representative sample and control any  extraneous variables that might influence your results. If random assignment of participants to control and treatment groups is impossible, unethical, or highly difficult, consider an observational study instead. This minimizes several types of research bias, particularly sampling bias , survivorship bias , and attrition bias as time passes.

Table of contents

Step 1: define your variables, step 2: write your hypothesis, step 3: design your experimental treatments, step 4: assign your subjects to treatment groups, step 5: measure your dependent variable, other interesting articles, frequently asked questions about experiments.

You should begin with a specific research question . We will work with two research question examples, one from health sciences and one from ecology:

To translate your research question into an experimental hypothesis, you need to define the main variables and make predictions about how they are related.

Start by simply listing the independent and dependent variables .

Then you need to think about possible extraneous and confounding variables and consider how you might control  them in your experiment.

Finally, you can put these variables together into a diagram. Use arrows to show the possible relationships between variables and include signs to show the expected direction of the relationships.

Here we predict that increasing temperature will increase soil respiration and decrease soil moisture, while decreasing soil moisture will lead to decreased soil respiration.

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Now that you have a strong conceptual understanding of the system you are studying, you should be able to write a specific, testable hypothesis that addresses your research question.

The next steps will describe how to design a controlled experiment . In a controlled experiment, you must be able to:

• Systematically and precisely manipulate the independent variable(s).
• Precisely measure the dependent variable(s).
• Control any potential confounding variables.

If your study system doesn’t match these criteria, there are other types of research you can use to answer your research question.

How you manipulate the independent variable can affect the experiment’s external validity – that is, the extent to which the results can be generalized and applied to the broader world.

First, you may need to decide how widely to vary your independent variable.

• just slightly above the natural range for your study region.
• over a wider range of temperatures to mimic future warming.
• over an extreme range that is beyond any possible natural variation.

Second, you may need to choose how finely to vary your independent variable. Sometimes this choice is made for you by your experimental system, but often you will need to decide, and this will affect how much you can infer from your results.

• a categorical variable : either as binary (yes/no) or as levels of a factor (no phone use, low phone use, high phone use).
• a continuous variable (minutes of phone use measured every night).

How you apply your experimental treatments to your test subjects is crucial for obtaining valid and reliable results.

First, you need to consider the study size : how many individuals will be included in the experiment? In general, the more subjects you include, the greater your experiment’s statistical power , which determines how much confidence you can have in your results.

Then you need to randomly assign your subjects to treatment groups . Each group receives a different level of the treatment (e.g. no phone use, low phone use, high phone use).

You should also include a control group , which receives no treatment. The control group tells us what would have happened to your test subjects without any experimental intervention.

When assigning your subjects to groups, there are two main choices you need to make:

• A completely randomized design vs a randomized block design .
• A between-subjects design vs a within-subjects design .

Randomization

An experiment can be completely randomized or randomized within blocks (aka strata):

• In a completely randomized design , every subject is assigned to a treatment group at random.
• In a randomized block design (aka stratified random design), subjects are first grouped according to a characteristic they share, and then randomly assigned to treatments within those groups.

Sometimes randomization isn’t practical or ethical , so researchers create partially-random or even non-random designs. An experimental design where treatments aren’t randomly assigned is called a quasi-experimental design .

Between-subjects vs. within-subjects

In a between-subjects design (also known as an independent measures design or classic ANOVA design), individuals receive only one of the possible levels of an experimental treatment.

In medical or social research, you might also use matched pairs within your between-subjects design to make sure that each treatment group contains the same variety of test subjects in the same proportions.

In a within-subjects design (also known as a repeated measures design), every individual receives each of the experimental treatments consecutively, and their responses to each treatment are measured.

Within-subjects or repeated measures can also refer to an experimental design where an effect emerges over time, and individual responses are measured over time in order to measure this effect as it emerges.

Counterbalancing (randomizing or reversing the order of treatments among subjects) is often used in within-subjects designs to ensure that the order of treatment application doesn’t influence the results of the experiment.

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Finally, you need to decide how you’ll collect data on your dependent variable outcomes. You should aim for reliable and valid measurements that minimize research bias or error.

Some variables, like temperature, can be objectively measured with scientific instruments. Others may need to be operationalized to turn them into measurable observations.

• Ask participants to record what time they go to sleep and get up each day.
• Ask participants to wear a sleep tracker.

How precisely you measure your dependent variable also affects the kinds of statistical analysis you can use on your data.

Experiments are always context-dependent, and a good experimental design will take into account all of the unique considerations of your study system to produce information that is both valid and relevant to your research question.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Student’s  t -distribution
• Normal distribution
• Null and Alternative Hypotheses
• Chi square tests
• Confidence interval
• Cluster sampling
• Stratified sampling
• Data cleansing
• Reproducibility vs Replicability
• Peer review
• Likert scale

Research bias

• Implicit bias
• Framing effect
• Cognitive bias
• Placebo effect
• Hawthorne effect
• Hindsight bias
• Affect heuristic

Experimental design means planning a set of procedures to investigate a relationship between variables . To design a controlled experiment, you need:

• A testable hypothesis
• At least one independent variable that can be precisely manipulated
• At least one dependent variable that can be precisely measured

When designing the experiment, you decide:

• How you will manipulate the variable(s)
• How you will control for any potential confounding variables
• How many subjects or samples will be included in the study
• How subjects will be assigned to treatment levels

Experimental design is essential to the internal and external validity of your experiment.

The key difference between observational studies and experimental designs is that a well-done observational study does not influence the responses of participants, while experiments do have some sort of treatment condition applied to at least some participants by random assignment .

A confounding variable , also called a confounder or confounding factor, is a third variable in a study examining a potential cause-and-effect relationship.

A confounding variable is related to both the supposed cause and the supposed effect of the study. It can be difficult to separate the true effect of the independent variable from the effect of the confounding variable.

In your research design , it’s important to identify potential confounding variables and plan how you will reduce their impact.

In a between-subjects design , every participant experiences only one condition, and researchers assess group differences between participants in various conditions.

In a within-subjects design , each participant experiences all conditions, and researchers test the same participants repeatedly for differences between conditions.

The word “between” means that you’re comparing different conditions between groups, while the word “within” means you’re comparing different conditions within the same group.

An experimental group, also known as a treatment group, receives the treatment whose effect researchers wish to study, whereas a control group does not. They should be identical in all other ways.

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19+ Experimental Design Examples (Methods + Types)

Ever wondered how scientists discover new medicines, psychologists learn about behavior, or even how marketers figure out what kind of ads you like? Well, they all have something in common: they use a special plan or recipe called an "experimental design."

Imagine you're baking cookies. You can't just throw random amounts of flour, sugar, and chocolate chips into a bowl and hope for the best. You follow a recipe, right? Scientists and researchers do something similar. They follow a "recipe" called an experimental design to make sure their experiments are set up in a way that the answers they find are meaningful and reliable.

Experimental design is the roadmap researchers use to answer questions. It's a set of rules and steps that researchers follow to collect information, or "data," in a way that is fair, accurate, and makes sense.

Long ago, people didn't have detailed game plans for experiments. They often just tried things out and saw what happened. But over time, people got smarter about this. They started creating structured plans—what we now call experimental designs—to get clearer, more trustworthy answers to their questions.

In this article, we'll take you on a journey through the world of experimental designs. We'll talk about the different types, or "flavors," of experimental designs, where they're used, and even give you a peek into how they came to be.

What Is Experimental Design?

Alright, before we dive into the different types of experimental designs, let's get crystal clear on what experimental design actually is.

Imagine you're a detective trying to solve a mystery. You need clues, right? Well, in the world of research, experimental design is like the roadmap that helps you find those clues. It's like the game plan in sports or the blueprint when you're building a house. Just like you wouldn't start building without a good blueprint, researchers won't start their studies without a strong experimental design.

So, why do we need experimental design? Think about baking a cake. If you toss ingredients into a bowl without measuring, you'll end up with a mess instead of a tasty dessert.

Similarly, in research, if you don't have a solid plan, you might get confusing or incorrect results. A good experimental design helps you ask the right questions ( think critically ), decide what to measure ( come up with an idea ), and figure out how to measure it (test it). It also helps you consider things that might mess up your results, like outside influences you hadn't thought of.

For example, let's say you want to find out if listening to music helps people focus better. Your experimental design would help you decide things like: Who are you going to test? What kind of music will you use? How will you measure focus? And, importantly, how will you make sure that it's really the music affecting focus and not something else, like the time of day or whether someone had a good breakfast?

In short, experimental design is the master plan that guides researchers through the process of collecting data, so they can answer questions in the most reliable way possible. It's like the GPS for the journey of discovery!

History of Experimental Design

Around 350 BCE, people like Aristotle were trying to figure out how the world works, but they mostly just thought really hard about things. They didn't test their ideas much. So while they were super smart, their methods weren't always the best for finding out the truth.

Fast forward to the Renaissance (14th to 17th centuries), a time of big changes and lots of curiosity. People like Galileo started to experiment by actually doing tests, like rolling balls down inclined planes to study motion. Galileo's work was cool because he combined thinking with doing. He'd have an idea, test it, look at the results, and then think some more. This approach was a lot more reliable than just sitting around and thinking.

Now, let's zoom ahead to the 18th and 19th centuries. This is when people like Francis Galton, an English polymath, started to get really systematic about experimentation. Galton was obsessed with measuring things. Seriously, he even tried to measure how good-looking people were ! His work helped create the foundations for a more organized approach to experiments.

Next stop: the early 20th century. Enter Ronald A. Fisher , a brilliant British statistician. Fisher was a game-changer. He came up with ideas that are like the bread and butter of modern experimental design.

Fisher invented the concept of the " control group "—that's a group of people or things that don't get the treatment you're testing, so you can compare them to those who do. He also stressed the importance of " randomization ," which means assigning people or things to different groups by chance, like drawing names out of a hat. This makes sure the experiment is fair and the results are trustworthy.

Around the same time, American psychologists like John B. Watson and B.F. Skinner were developing " behaviorism ." They focused on studying things that they could directly observe and measure, like actions and reactions.

Skinner even built boxes—called Skinner Boxes —to test how animals like pigeons and rats learn. Their work helped shape how psychologists design experiments today. Watson performed a very controversial experiment called The Little Albert experiment that helped describe behaviour through conditioning—in other words, how people learn to behave the way they do.

In the later part of the 20th century and into our time, computers have totally shaken things up. Researchers now use super powerful software to help design their experiments and crunch the numbers.

With computers, they can simulate complex experiments before they even start, which helps them predict what might happen. This is especially helpful in fields like medicine, where getting things right can be a matter of life and death.

Also, did you know that experimental designs aren't just for scientists in labs? They're used by people in all sorts of jobs, like marketing, education, and even video game design! Yes, someone probably ran an experiment to figure out what makes a game super fun to play.

So there you have it—a quick tour through the history of experimental design, from Aristotle's deep thoughts to Fisher's groundbreaking ideas, and all the way to today's computer-powered research. These designs are the recipes that help people from all walks of life find answers to their big questions.

Key Terms in Experimental Design

Before we dig into the different types of experimental designs, let's get comfy with some key terms. Understanding these terms will make it easier for us to explore the various types of experimental designs that researchers use to answer their big questions.

Independent Variable : This is what you change or control in your experiment to see what effect it has. Think of it as the "cause" in a cause-and-effect relationship. For example, if you're studying whether different types of music help people focus, the kind of music is the independent variable.

Dependent Variable : This is what you're measuring to see the effect of your independent variable. In our music and focus experiment, how well people focus is the dependent variable—it's what "depends" on the kind of music played.

Control Group : This is a group of people who don't get the special treatment or change you're testing. They help you see what happens when the independent variable is not applied. If you're testing whether a new medicine works, the control group would take a fake pill, called a placebo , instead of the real medicine.

Experimental Group : This is the group that gets the special treatment or change you're interested in. Going back to our medicine example, this group would get the actual medicine to see if it has any effect.

Randomization : This is like shaking things up in a fair way. You randomly put people into the control or experimental group so that each group is a good mix of different kinds of people. This helps make the results more reliable.

Sample : This is the group of people you're studying. They're a "sample" of a larger group that you're interested in. For instance, if you want to know how teenagers feel about a new video game, you might study a sample of 100 teenagers.

Bias : This is anything that might tilt your experiment one way or another without you realizing it. Like if you're testing a new kind of dog food and you only test it on poodles, that could create a bias because maybe poodles just really like that food and other breeds don't.

Data : This is the information you collect during the experiment. It's like the treasure you find on your journey of discovery!

Replication : This means doing the experiment more than once to make sure your findings hold up. It's like double-checking your answers on a test.

Hypothesis : This is your educated guess about what will happen in the experiment. It's like predicting the end of a movie based on the first half.

Steps of Experimental Design

Alright, let's say you're all fired up and ready to run your own experiment. Cool! But where do you start? Well, designing an experiment is a bit like planning a road trip. There are some key steps you've got to take to make sure you reach your destination. Let's break it down:

• Ask a Question : Before you hit the road, you've got to know where you're going. Same with experiments. You start with a question you want to answer, like "Does eating breakfast really make you do better in school?"
• Do Some Homework : Before you pack your bags, you look up the best places to visit, right? In science, this means reading up on what other people have already discovered about your topic.
• Form a Hypothesis : This is your educated guess about what you think will happen. It's like saying, "I bet this route will get us there faster."
• Plan the Details : Now you decide what kind of car you're driving (your experimental design), who's coming with you (your sample), and what snacks to bring (your variables).
• Randomization : Remember, this is like shuffling a deck of cards. You want to mix up who goes into your control and experimental groups to make sure it's a fair test.
• Run the Experiment : Finally, the rubber hits the road! You carry out your plan, making sure to collect your data carefully.
• Analyze the Data : Once the trip's over, you look at your photos and decide which ones are keepers. In science, this means looking at your data to see what it tells you.
• Draw Conclusions : Based on your data, did you find an answer to your question? This is like saying, "Yep, that route was faster," or "Nope, we hit a ton of traffic."
• Share Your Findings : After a great trip, you want to tell everyone about it, right? Scientists do the same by publishing their results so others can learn from them.
• Do It Again? : Sometimes one road trip just isn't enough. In the same way, scientists often repeat their experiments to make sure their findings are solid.

So there you have it! Those are the basic steps you need to follow when you're designing an experiment. Each step helps make sure that you're setting up a fair and reliable way to find answers to your big questions.

Let's get into examples of experimental designs.

1) True Experimental Design

In the world of experiments, the True Experimental Design is like the superstar quarterback everyone talks about. Born out of the early 20th-century work of statisticians like Ronald A. Fisher, this design is all about control, precision, and reliability.

Researchers carefully pick an independent variable to manipulate (remember, that's the thing they're changing on purpose) and measure the dependent variable (the effect they're studying). Then comes the magic trick—randomization. By randomly putting participants into either the control or experimental group, scientists make sure their experiment is as fair as possible.

No sneaky biases here!

True Experimental Design Pros

The pros of True Experimental Design are like the perks of a VIP ticket at a concert: you get the best and most trustworthy results. Because everything is controlled and randomized, you can feel pretty confident that the results aren't just a fluke.

True Experimental Design Cons

However, there's a catch. Sometimes, it's really tough to set up these experiments in a real-world situation. Imagine trying to control every single detail of your day, from the food you eat to the air you breathe. Not so easy, right?

True Experimental Design Uses

The fields that get the most out of True Experimental Designs are those that need super reliable results, like medical research.

When scientists were developing COVID-19 vaccines, they used this design to run clinical trials. They had control groups that received a placebo (a harmless substance with no effect) and experimental groups that got the actual vaccine. Then they measured how many people in each group got sick. By comparing the two, they could say, "Yep, this vaccine works!"

So next time you read about a groundbreaking discovery in medicine or technology, chances are a True Experimental Design was the VIP behind the scenes, making sure everything was on point. It's been the go-to for rigorous scientific inquiry for nearly a century, and it's not stepping off the stage anytime soon.

2) Quasi-Experimental Design

So, let's talk about the Quasi-Experimental Design. Think of this one as the cool cousin of True Experimental Design. It wants to be just like its famous relative, but it's a bit more laid-back and flexible. You'll find quasi-experimental designs when it's tricky to set up a full-blown True Experimental Design with all the bells and whistles.

Quasi-experiments still play with an independent variable, just like their stricter cousins. The big difference? They don't use randomization. It's like wanting to divide a bag of jelly beans equally between your friends, but you can't quite do it perfectly.

In real life, it's often not possible or ethical to randomly assign people to different groups, especially when dealing with sensitive topics like education or social issues. And that's where quasi-experiments come in.

Quasi-Experimental Design Pros

Even though they lack full randomization, quasi-experimental designs are like the Swiss Army knives of research: versatile and practical. They're especially popular in fields like education, sociology, and public policy.

For instance, when researchers wanted to figure out if the Head Start program , aimed at giving young kids a "head start" in school, was effective, they used a quasi-experimental design. They couldn't randomly assign kids to go or not go to preschool, but they could compare kids who did with kids who didn't.

Quasi-Experimental Design Cons

Of course, quasi-experiments come with their own bag of pros and cons. On the plus side, they're easier to set up and often cheaper than true experiments. But the flip side is that they're not as rock-solid in their conclusions. Because the groups aren't randomly assigned, there's always that little voice saying, "Hey, are we missing something here?"

Quasi-Experimental Design Uses

Quasi-Experimental Design gained traction in the mid-20th century. Researchers were grappling with real-world problems that didn't fit neatly into a laboratory setting. Plus, as society became more aware of ethical considerations, the need for flexible designs increased. So, the quasi-experimental approach was like a breath of fresh air for scientists wanting to study complex issues without a laundry list of restrictions.

In short, if True Experimental Design is the superstar quarterback, Quasi-Experimental Design is the versatile player who can adapt and still make significant contributions to the game.

3) Pre-Experimental Design

Now, let's talk about the Pre-Experimental Design. Imagine it as the beginner's skateboard you get before you try out for all the cool tricks. It has wheels, it rolls, but it's not built for the professional skatepark.

Similarly, pre-experimental designs give researchers a starting point. They let you dip your toes in the water of scientific research without diving in head-first.

So, what's the deal with pre-experimental designs?

Pre-Experimental Designs are the basic, no-frills versions of experiments. Researchers still mess around with an independent variable and measure a dependent variable, but they skip over the whole randomization thing and often don't even have a control group.

It's like baking a cake but forgetting the frosting and sprinkles; you'll get some results, but they might not be as complete or reliable as you'd like.

Pre-Experimental Design Pros

Why use such a simple setup? Because sometimes, you just need to get the ball rolling. Pre-experimental designs are great for quick-and-dirty research when you're short on time or resources. They give you a rough idea of what's happening, which you can use to plan more detailed studies later.

A good example of this is early studies on the effects of screen time on kids. Researchers couldn't control every aspect of a child's life, but they could easily ask parents to track how much time their kids spent in front of screens and then look for trends in behavior or school performance.

Pre-Experimental Design Cons

But here's the catch: pre-experimental designs are like that first draft of an essay. It helps you get your ideas down, but you wouldn't want to turn it in for a grade. Because these designs lack the rigorous structure of true or quasi-experimental setups, they can't give you rock-solid conclusions. They're more like clues or signposts pointing you in a certain direction.

Pre-Experimental Design Uses

This type of design became popular in the early stages of various scientific fields. Researchers used them to scratch the surface of a topic, generate some initial data, and then decide if it's worth exploring further. In other words, pre-experimental designs were the stepping stones that led to more complex, thorough investigations.

So, while Pre-Experimental Design may not be the star player on the team, it's like the practice squad that helps everyone get better. It's the starting point that can lead to bigger and better things.

4) Factorial Design

Now, buckle up, because we're moving into the world of Factorial Design, the multi-tasker of the experimental universe.

Imagine juggling not just one, but multiple balls in the air—that's what researchers do in a factorial design.

In Factorial Design, researchers are not satisfied with just studying one independent variable. Nope, they want to study two or more at the same time to see how they interact.

It's like cooking with several spices to see how they blend together to create unique flavors.

Factorial Design became the talk of the town with the rise of computers. Why? Because this design produces a lot of data, and computers are the number crunchers that help make sense of it all. So, thanks to our silicon friends, researchers can study complicated questions like, "How do diet AND exercise together affect weight loss?" instead of looking at just one of those factors.

Factorial Design Pros

This design's main selling point is its ability to explore interactions between variables. For instance, maybe a new study drug works really well for young people but not so great for older adults. A factorial design could reveal that age is a crucial factor, something you might miss if you only studied the drug's effectiveness in general. It's like being a detective who looks for clues not just in one room but throughout the entire house.

Factorial Design Cons

However, factorial designs have their own bag of challenges. First off, they can be pretty complicated to set up and run. Imagine coordinating a four-way intersection with lots of cars coming from all directions—you've got to make sure everything runs smoothly, or you'll end up with a traffic jam. Similarly, researchers need to carefully plan how they'll measure and analyze all the different variables.

Factorial Design Uses

Factorial designs are widely used in psychology to untangle the web of factors that influence human behavior. They're also popular in fields like marketing, where companies want to understand how different aspects like price, packaging, and advertising influence a product's success.

And speaking of success, the factorial design has been a hit since statisticians like Ronald A. Fisher (yep, him again!) expanded on it in the early-to-mid 20th century. It offered a more nuanced way of understanding the world, proving that sometimes, to get the full picture, you've got to juggle more than one ball at a time.

So, if True Experimental Design is the quarterback and Quasi-Experimental Design is the versatile player, Factorial Design is the strategist who sees the entire game board and makes moves accordingly.

5) Longitudinal Design

Alright, let's take a step into the world of Longitudinal Design. Picture it as the grand storyteller, the kind who doesn't just tell you about a single event but spins an epic tale that stretches over years or even decades. This design isn't about quick snapshots; it's about capturing the whole movie of someone's life or a long-running process.

You know how you might take a photo every year on your birthday to see how you've changed? Longitudinal Design is kind of like that, but for scientific research.

With Longitudinal Design, instead of measuring something just once, researchers come back again and again, sometimes over many years, to see how things are going. This helps them understand not just what's happening, but why it's happening and how it changes over time.

This design really started to shine in the latter half of the 20th century, when researchers began to realize that some questions can't be answered in a hurry. Think about studies that look at how kids grow up, or research on how a certain medicine affects you over a long period. These aren't things you can rush.

The famous Framingham Heart Study , started in 1948, is a prime example. It's been studying heart health in a small town in Massachusetts for decades, and the findings have shaped what we know about heart disease.

Longitudinal Design Pros

So, what's to love about Longitudinal Design? First off, it's the go-to for studying change over time, whether that's how people age or how a forest recovers from a fire.

Longitudinal Design Cons

But it's not all sunshine and rainbows. Longitudinal studies take a lot of patience and resources. Plus, keeping track of participants over many years can be like herding cats—difficult and full of surprises.

Longitudinal Design Uses

Despite these challenges, longitudinal studies have been key in fields like psychology, sociology, and medicine. They provide the kind of deep, long-term insights that other designs just can't match.

So, if the True Experimental Design is the superstar quarterback, and the Quasi-Experimental Design is the flexible athlete, then the Factorial Design is the strategist, and the Longitudinal Design is the wise elder who has seen it all and has stories to tell.

6) Cross-Sectional Design

Now, let's flip the script and talk about Cross-Sectional Design, the polar opposite of the Longitudinal Design. If Longitudinal is the grand storyteller, think of Cross-Sectional as the snapshot photographer. It captures a single moment in time, like a selfie that you take to remember a fun day. Researchers using this design collect all their data at one point, providing a kind of "snapshot" of whatever they're studying.

In a Cross-Sectional Design, researchers look at multiple groups all at the same time to see how they're different or similar.

This design rose to popularity in the mid-20th century, mainly because it's so quick and efficient. Imagine wanting to know how people of different ages feel about a new video game. Instead of waiting for years to see how opinions change, you could just ask people of all ages what they think right now. That's Cross-Sectional Design for you—fast and straightforward.

You'll find this type of research everywhere from marketing studies to healthcare. For instance, you might have heard about surveys asking people what they think about a new product or political issue. Those are usually cross-sectional studies, aimed at getting a quick read on public opinion.

Cross-Sectional Design Pros

So, what's the big deal with Cross-Sectional Design? Well, it's the go-to when you need answers fast and don't have the time or resources for a more complicated setup.

Cross-Sectional Design Cons

Remember, speed comes with trade-offs. While you get your results quickly, those results are stuck in time. They can't tell you how things change or why they're changing, just what's happening right now.

Cross-Sectional Design Uses

Also, because they're so quick and simple, cross-sectional studies often serve as the first step in research. They give scientists an idea of what's going on so they can decide if it's worth digging deeper. In that way, they're a bit like a movie trailer, giving you a taste of the action to see if you're interested in seeing the whole film.

So, in our lineup of experimental designs, if True Experimental Design is the superstar quarterback and Longitudinal Design is the wise elder, then Cross-Sectional Design is like the speedy running back—fast, agile, but not designed for long, drawn-out plays.

7) Correlational Design

Next on our roster is the Correlational Design, the keen observer of the experimental world. Imagine this design as the person at a party who loves people-watching. They don't interfere or get involved; they just observe and take mental notes about what's going on.

In a correlational study, researchers don't change or control anything; they simply observe and measure how two variables relate to each other.

The correlational design has roots in the early days of psychology and sociology. Pioneers like Sir Francis Galton used it to study how qualities like intelligence or height could be related within families.

This design is all about asking, "Hey, when this thing happens, does that other thing usually happen too?" For example, researchers might study whether students who have more study time get better grades or whether people who exercise more have lower stress levels.

One of the most famous correlational studies you might have heard of is the link between smoking and lung cancer. Back in the mid-20th century, researchers started noticing that people who smoked a lot also seemed to get lung cancer more often. They couldn't say smoking caused cancer—that would require a true experiment—but the strong correlation was a red flag that led to more research and eventually, health warnings.

Correlational Design Pros

This design is great at proving that two (or more) things can be related. Correlational designs can help prove that more detailed research is needed on a topic. They can help us see patterns or possible causes for things that we otherwise might not have realized.

Correlational Design Cons

But here's where you need to be careful: correlational designs can be tricky. Just because two things are related doesn't mean one causes the other. That's like saying, "Every time I wear my lucky socks, my team wins." Well, it's a fun thought, but those socks aren't really controlling the game.

Correlational Design Uses

Despite this limitation, correlational designs are popular in psychology, economics, and epidemiology, to name a few fields. They're often the first step in exploring a possible relationship between variables. Once a strong correlation is found, researchers may decide to conduct more rigorous experimental studies to examine cause and effect.

So, if the True Experimental Design is the superstar quarterback and the Longitudinal Design is the wise elder, the Factorial Design is the strategist, and the Cross-Sectional Design is the speedster, then the Correlational Design is the clever scout, identifying interesting patterns but leaving the heavy lifting of proving cause and effect to the other types of designs.

8) Meta-Analysis

Last but not least, let's talk about Meta-Analysis, the librarian of experimental designs.

If other designs are all about creating new research, Meta-Analysis is about gathering up everyone else's research, sorting it, and figuring out what it all means when you put it together.

Imagine a jigsaw puzzle where each piece is a different study. Meta-Analysis is the process of fitting all those pieces together to see the big picture.

The concept of Meta-Analysis started to take shape in the late 20th century, when computers became powerful enough to handle massive amounts of data. It was like someone handed researchers a super-powered magnifying glass, letting them examine multiple studies at the same time to find common trends or results.

You might have heard of the Cochrane Reviews in healthcare . These are big collections of meta-analyses that help doctors and policymakers figure out what treatments work best based on all the research that's been done.

For example, if ten different studies show that a certain medicine helps lower blood pressure, a meta-analysis would pull all that information together to give a more accurate answer.

Meta-Analysis Pros

The beauty of Meta-Analysis is that it can provide really strong evidence. Instead of relying on one study, you're looking at the whole landscape of research on a topic.

Meta-Analysis Cons

However, it does have some downsides. For one, Meta-Analysis is only as good as the studies it includes. If those studies are flawed, the meta-analysis will be too. It's like baking a cake: if you use bad ingredients, it doesn't matter how good your recipe is—the cake won't turn out well.

Meta-Analysis Uses

Despite these challenges, meta-analyses are highly respected and widely used in many fields like medicine, psychology, and education. They help us make sense of a world that's bursting with information by showing us the big picture drawn from many smaller snapshots.

So, in our all-star lineup, if True Experimental Design is the quarterback and Longitudinal Design is the wise elder, the Factorial Design is the strategist, the Cross-Sectional Design is the speedster, and the Correlational Design is the scout, then the Meta-Analysis is like the coach, using insights from everyone else's plays to come up with the best game plan.

9) Non-Experimental Design

Now, let's talk about a player who's a bit of an outsider on this team of experimental designs—the Non-Experimental Design. Think of this design as the commentator or the journalist who covers the game but doesn't actually play.

In a Non-Experimental Design, researchers are like reporters gathering facts, but they don't interfere or change anything. They're simply there to describe and analyze.

Non-Experimental Design Pros

So, what's the deal with Non-Experimental Design? Its strength is in description and exploration. It's really good for studying things as they are in the real world, without changing any conditions.

Non-Experimental Design Cons

Because a non-experimental design doesn't manipulate variables, it can't prove cause and effect. It's like a weather reporter: they can tell you it's raining, but they can't tell you why it's raining.

The downside? Since researchers aren't controlling variables, it's hard to rule out other explanations for what they observe. It's like hearing one side of a story—you get an idea of what happened, but it might not be the complete picture.

Non-Experimental Design Uses

Non-Experimental Design has always been a part of research, especially in fields like anthropology, sociology, and some areas of psychology.

For instance, if you've ever heard of studies that describe how people behave in different cultures or what teens like to do in their free time, that's often Non-Experimental Design at work. These studies aim to capture the essence of a situation, like painting a portrait instead of taking a snapshot.

One well-known example you might have heard about is the Kinsey Reports from the 1940s and 1950s, which described sexual behavior in men and women. Researchers interviewed thousands of people but didn't manipulate any variables like you would in a true experiment. They simply collected data to create a comprehensive picture of the subject matter.

So, in our metaphorical team of research designs, if True Experimental Design is the quarterback and Longitudinal Design is the wise elder, Factorial Design is the strategist, Cross-Sectional Design is the speedster, Correlational Design is the scout, and Meta-Analysis is the coach, then Non-Experimental Design is the sports journalist—always present, capturing the game, but not part of the action itself.

10) Repeated Measures Design

Time to meet the Repeated Measures Design, the time traveler of our research team. If this design were a player in a sports game, it would be the one who keeps revisiting past plays to figure out how to improve the next one.

Repeated Measures Design is all about studying the same people or subjects multiple times to see how they change or react under different conditions.

The idea behind Repeated Measures Design isn't new; it's been around since the early days of psychology and medicine. You could say it's a cousin to the Longitudinal Design, but instead of looking at how things naturally change over time, it focuses on how the same group reacts to different things.

Imagine a study looking at how a new energy drink affects people's running speed. Instead of comparing one group that drank the energy drink to another group that didn't, a Repeated Measures Design would have the same group of people run multiple times—once with the energy drink, and once without. This way, you're really zeroing in on the effect of that energy drink, making the results more reliable.

Repeated Measures Design Pros

The strong point of Repeated Measures Design is that it's super focused. Because it uses the same subjects, you don't have to worry about differences between groups messing up your results.

Repeated Measures Design Cons

But the downside? Well, people can get tired or bored if they're tested too many times, which might affect how they respond.

Repeated Measures Design Uses

A famous example of this design is the "Little Albert" experiment, conducted by John B. Watson and Rosalie Rayner in 1920. In this study, a young boy was exposed to a white rat and other stimuli several times to see how his emotional responses changed. Though the ethical standards of this experiment are often criticized today, it was groundbreaking in understanding conditioned emotional responses.

In our metaphorical lineup of research designs, if True Experimental Design is the quarterback and Longitudinal Design is the wise elder, Factorial Design is the strategist, Cross-Sectional Design is the speedster, Correlational Design is the scout, Meta-Analysis is the coach, and Non-Experimental Design is the journalist, then Repeated Measures Design is the time traveler—always looping back to fine-tune the game plan.

11) Crossover Design

Next up is Crossover Design, the switch-hitter of the research world. If you're familiar with baseball, you'll know a switch-hitter is someone who can bat both right-handed and left-handed.

In a similar way, Crossover Design allows subjects to experience multiple conditions, flipping them around so that everyone gets a turn in each role.

This design is like the utility player on our team—versatile, flexible, and really good at adapting.

The Crossover Design has its roots in medical research and has been popular since the mid-20th century. It's often used in clinical trials to test the effectiveness of different treatments.

Crossover Design Pros

The neat thing about this design is that it allows each participant to serve as their own control group. Imagine you're testing two new kinds of headache medicine. Instead of giving one type to one group and another type to a different group, you'd give both kinds to the same people but at different times.

Crossover Design Cons

What's the big deal with Crossover Design? Its major strength is in reducing the "noise" that comes from individual differences. Since each person experiences all conditions, it's easier to see real effects. However, there's a catch. This design assumes that there's no lasting effect from the first condition when you switch to the second one. That might not always be true. If the first treatment has a long-lasting effect, it could mess up the results when you switch to the second treatment.

Crossover Design Uses

A well-known example of Crossover Design is in studies that look at the effects of different types of diets—like low-carb vs. low-fat diets. Researchers might have participants follow a low-carb diet for a few weeks, then switch them to a low-fat diet. By doing this, they can more accurately measure how each diet affects the same group of people.

In our team of experimental designs, if True Experimental Design is the quarterback and Longitudinal Design is the wise elder, Factorial Design is the strategist, Cross-Sectional Design is the speedster, Correlational Design is the scout, Meta-Analysis is the coach, Non-Experimental Design is the journalist, and Repeated Measures Design is the time traveler, then Crossover Design is the versatile utility player—always ready to adapt and play multiple roles to get the most accurate results.

12) Cluster Randomized Design

Meet the Cluster Randomized Design, the team captain of group-focused research. In our imaginary lineup of experimental designs, if other designs focus on individual players, then Cluster Randomized Design is looking at how the entire team functions.

This approach is especially common in educational and community-based research, and it's been gaining traction since the late 20th century.

Here's how Cluster Randomized Design works: Instead of assigning individual people to different conditions, researchers assign entire groups, or "clusters." These could be schools, neighborhoods, or even entire towns. This helps you see how the new method works in a real-world setting.

Imagine you want to see if a new anti-bullying program really works. Instead of selecting individual students, you'd introduce the program to a whole school or maybe even several schools, and then compare the results to schools without the program.

Cluster Randomized Design Pros

Why use Cluster Randomized Design? Well, sometimes it's just not practical to assign conditions at the individual level. For example, you can't really have half a school following a new reading program while the other half sticks with the old one; that would be way too confusing! Cluster Randomization helps get around this problem by treating each "cluster" as its own mini-experiment.

Cluster Randomized Design Cons

There's a downside, too. Because entire groups are assigned to each condition, there's a risk that the groups might be different in some important way that the researchers didn't account for. That's like having one sports team that's full of veterans playing against a team of rookies; the match wouldn't be fair.

Cluster Randomized Design Uses

A famous example is the research conducted to test the effectiveness of different public health interventions, like vaccination programs. Researchers might roll out a vaccination program in one community but not in another, then compare the rates of disease in both.

In our metaphorical research team, if True Experimental Design is the quarterback, Longitudinal Design is the wise elder, Factorial Design is the strategist, Cross-Sectional Design is the speedster, Correlational Design is the scout, Meta-Analysis is the coach, Non-Experimental Design is the journalist, Repeated Measures Design is the time traveler, and Crossover Design is the utility player, then Cluster Randomized Design is the team captain—always looking out for the group as a whole.

13) Mixed-Methods Design

Say hello to Mixed-Methods Design, the all-rounder or the "Renaissance player" of our research team.

Mixed-Methods Design uses a blend of both qualitative and quantitative methods to get a more complete picture, just like a Renaissance person who's good at lots of different things. It's like being good at both offense and defense in a sport; you've got all your bases covered!

Mixed-Methods Design is a fairly new kid on the block, becoming more popular in the late 20th and early 21st centuries as researchers began to see the value in using multiple approaches to tackle complex questions. It's the Swiss Army knife in our research toolkit, combining the best parts of other designs to be more versatile.

Here's how it could work: Imagine you're studying the effects of a new educational app on students' math skills. You might use quantitative methods like tests and grades to measure how much the students improve—that's the 'numbers part.'

But you also want to know how the students feel about math now, or why they think they got better or worse. For that, you could conduct interviews or have students fill out journals—that's the 'story part.'

Mixed-Methods Design Pros

So, what's the scoop on Mixed-Methods Design? The strength is its versatility and depth; you're not just getting numbers or stories, you're getting both, which gives a fuller picture.

Mixed-Methods Design Cons

But, it's also more challenging. Imagine trying to play two sports at the same time! You have to be skilled in different research methods and know how to combine them effectively.

Mixed-Methods Design Uses

A high-profile example of Mixed-Methods Design is research on climate change. Scientists use numbers and data to show temperature changes (quantitative), but they also interview people to understand how these changes are affecting communities (qualitative).

In our team of experimental designs, if True Experimental Design is the quarterback, Longitudinal Design is the wise elder, Factorial Design is the strategist, Cross-Sectional Design is the speedster, Correlational Design is the scout, Meta-Analysis is the coach, Non-Experimental Design is the journalist, Repeated Measures Design is the time traveler, Crossover Design is the utility player, and Cluster Randomized Design is the team captain, then Mixed-Methods Design is the Renaissance player—skilled in multiple areas and able to bring them all together for a winning strategy.

14) Multivariate Design

Now, let's turn our attention to Multivariate Design, the multitasker of the research world.

If our lineup of research designs were like players on a basketball court, Multivariate Design would be the player dribbling, passing, and shooting all at once. This design doesn't just look at one or two things; it looks at several variables simultaneously to see how they interact and affect each other.

Multivariate Design is like baking a cake with many ingredients. Instead of just looking at how flour affects the cake, you also consider sugar, eggs, and milk all at once. This way, you understand how everything works together to make the cake taste good or bad.

Multivariate Design has been a go-to method in psychology, economics, and social sciences since the latter half of the 20th century. With the advent of computers and advanced statistical software, analyzing multiple variables at once became a lot easier, and Multivariate Design soared in popularity.

Multivariate Design Pros

So, what's the benefit of using Multivariate Design? Its power lies in its complexity. By studying multiple variables at the same time, you can get a really rich, detailed understanding of what's going on.

Multivariate Design Cons

But that complexity can also be a drawback. With so many variables, it can be tough to tell which ones are really making a difference and which ones are just along for the ride.

Multivariate Design Uses

Imagine you're a coach trying to figure out the best strategy to win games. You wouldn't just look at how many points your star player scores; you'd also consider assists, rebounds, turnovers, and maybe even how loud the crowd is. A Multivariate Design would help you understand how all these factors work together to determine whether you win or lose.

A well-known example of Multivariate Design is in market research. Companies often use this approach to figure out how different factors—like price, packaging, and advertising—affect sales. By studying multiple variables at once, they can find the best combination to boost profits.

In our metaphorical research team, if True Experimental Design is the quarterback, Longitudinal Design is the wise elder, Factorial Design is the strategist, Cross-Sectional Design is the speedster, Correlational Design is the scout, Meta-Analysis is the coach, Non-Experimental Design is the journalist, Repeated Measures Design is the time traveler, Crossover Design is the utility player, Cluster Randomized Design is the team captain, and Mixed-Methods Design is the Renaissance player, then Multivariate Design is the multitasker—juggling many variables at once to get a fuller picture of what's happening.

15) Pretest-Posttest Design

Let's introduce Pretest-Posttest Design, the "Before and After" superstar of our research team. You've probably seen those before-and-after pictures in ads for weight loss programs or home renovations, right?

Well, this design is like that, but for science! Pretest-Posttest Design checks out what things are like before the experiment starts and then compares that to what things are like after the experiment ends.

This design is one of the classics, a staple in research for decades across various fields like psychology, education, and healthcare. It's so simple and straightforward that it has stayed popular for a long time.

In Pretest-Posttest Design, you measure your subject's behavior or condition before you introduce any changes—that's your "before" or "pretest." Then you do your experiment, and after it's done, you measure the same thing again—that's your "after" or "posttest."

Pretest-Posttest Design Pros

What makes Pretest-Posttest Design special? It's pretty easy to understand and doesn't require fancy statistics.

Pretest-Posttest Design Cons

But there are some pitfalls. For example, what if the kids in our math example get better at multiplication just because they're older or because they've taken the test before? That would make it hard to tell if the program is really effective or not.

Pretest-Posttest Design Uses

Let's say you're a teacher and you want to know if a new math program helps kids get better at multiplication. First, you'd give all the kids a multiplication test—that's your pretest. Then you'd teach them using the new math program. At the end, you'd give them the same test again—that's your posttest. If the kids do better on the second test, you might conclude that the program works.

One famous use of Pretest-Posttest Design is in evaluating the effectiveness of driver's education courses. Researchers will measure people's driving skills before and after the course to see if they've improved.

16) Solomon Four-Group Design

Next up is the Solomon Four-Group Design, the "chess master" of our research team. This design is all about strategy and careful planning. Named after Richard L. Solomon who introduced it in the 1940s, this method tries to correct some of the weaknesses in simpler designs, like the Pretest-Posttest Design.

Here's how it rolls: The Solomon Four-Group Design uses four different groups to test a hypothesis. Two groups get a pretest, then one of them receives the treatment or intervention, and both get a posttest. The other two groups skip the pretest, and only one of them receives the treatment before they both get a posttest.

Sound complicated? It's like playing 4D chess; you're thinking several moves ahead!

Solomon Four-Group Design Pros

What's the pro and con of the Solomon Four-Group Design? On the plus side, it provides really robust results because it accounts for so many variables.

Solomon Four-Group Design Cons

The downside? It's a lot of work and requires a lot of participants, making it more time-consuming and costly.

Solomon Four-Group Design Uses

Let's say you want to figure out if a new way of teaching history helps students remember facts better. Two classes take a history quiz (pretest), then one class uses the new teaching method while the other sticks with the old way. Both classes take another quiz afterward (posttest).

Meanwhile, two more classes skip the initial quiz, and then one uses the new method before both take the final quiz. Comparing all four groups will give you a much clearer picture of whether the new teaching method works and whether the pretest itself affects the outcome.

The Solomon Four-Group Design is less commonly used than simpler designs but is highly respected for its ability to control for more variables. It's a favorite in educational and psychological research where you really want to dig deep and figure out what's actually causing changes.

17) Adaptive Designs

Now, let's talk about Adaptive Designs, the chameleons of the experimental world.

Imagine you're a detective, and halfway through solving a case, you find a clue that changes everything. You wouldn't just stick to your old plan; you'd adapt and change your approach, right? That's exactly what Adaptive Designs allow researchers to do.

In an Adaptive Design, researchers can make changes to the study as it's happening, based on early results. In a traditional study, once you set your plan, you stick to it from start to finish.

Adaptive Design Pros

This method is particularly useful in fast-paced or high-stakes situations, like developing a new vaccine in the middle of a pandemic. The ability to adapt can save both time and resources, and more importantly, it can save lives by getting effective treatments out faster.

Adaptive Design Cons

But Adaptive Designs aren't without their drawbacks. They can be very complex to plan and carry out, and there's always a risk that the changes made during the study could introduce bias or errors.

Adaptive Design Uses

Adaptive Designs are most often seen in clinical trials, particularly in the medical and pharmaceutical fields.

For instance, if a new drug is showing really promising results, the study might be adjusted to give more participants the new treatment instead of a placebo. Or if one dose level is showing bad side effects, it might be dropped from the study.

The best part is, these changes are pre-planned. Researchers lay out in advance what changes might be made and under what conditions, which helps keep everything scientific and above board.

In terms of applications, besides their heavy usage in medical and pharmaceutical research, Adaptive Designs are also becoming increasingly popular in software testing and market research. In these fields, being able to quickly adjust to early results can give companies a significant advantage.

Adaptive Designs are like the agile startups of the research world—quick to pivot, keen to learn from ongoing results, and focused on rapid, efficient progress. However, they require a great deal of expertise and careful planning to ensure that the adaptability doesn't compromise the integrity of the research.

18) Bayesian Designs

Next, let's dive into Bayesian Designs, the data detectives of the research universe. Named after Thomas Bayes, an 18th-century statistician and minister, this design doesn't just look at what's happening now; it also takes into account what's happened before.

Imagine if you were a detective who not only looked at the evidence in front of you but also used your past cases to make better guesses about your current one. That's the essence of Bayesian Designs.

Bayesian Designs are like detective work in science. As you gather more clues (or data), you update your best guess on what's really happening. This way, your experiment gets smarter as it goes along.

In the world of research, Bayesian Designs are most notably used in areas where you have some prior knowledge that can inform your current study. For example, if earlier research shows that a certain type of medicine usually works well for a specific illness, a Bayesian Design would include that information when studying a new group of patients with the same illness.

Bayesian Design Pros

One of the major advantages of Bayesian Designs is their efficiency. Because they use existing data to inform the current experiment, often fewer resources are needed to reach a reliable conclusion.

Bayesian Design Cons

However, they can be quite complicated to set up and require a deep understanding of both statistics and the subject matter at hand.

Bayesian Design Uses

Bayesian Designs are highly valued in medical research, finance, environmental science, and even in Internet search algorithms. Their ability to continually update and refine hypotheses based on new evidence makes them particularly useful in fields where data is constantly evolving and where quick, informed decisions are crucial.

Here's a real-world example: In the development of personalized medicine, where treatments are tailored to individual patients, Bayesian Designs are invaluable. If a treatment has been effective for patients with similar genetics or symptoms in the past, a Bayesian approach can use that data to predict how well it might work for a new patient.

This type of design is also increasingly popular in machine learning and artificial intelligence. In these fields, Bayesian Designs help algorithms "learn" from past data to make better predictions or decisions in new situations. It's like teaching a computer to be a detective that gets better and better at solving puzzles the more puzzles it sees.

19) Covariate Adaptive Randomization

Now let's turn our attention to Covariate Adaptive Randomization, which you can think of as the "matchmaker" of experimental designs.

Picture a soccer coach trying to create the most balanced teams for a friendly match. They wouldn't just randomly assign players; they'd take into account each player's skills, experience, and other traits.

Covariate Adaptive Randomization is all about creating the most evenly matched groups possible for an experiment.

In traditional randomization, participants are allocated to different groups purely by chance. This is a pretty fair way to do things, but it can sometimes lead to unbalanced groups.

Imagine if all the professional-level players ended up on one soccer team and all the beginners on another; that wouldn't be a very informative match! Covariate Adaptive Randomization fixes this by using important traits or characteristics (called "covariates") to guide the randomization process.

Covariate Adaptive Randomization Pros

The benefits of this design are pretty clear: it aims for balance and fairness, making the final results more trustworthy.

Covariate Adaptive Randomization Cons

But it's not perfect. It can be complex to implement and requires a deep understanding of which characteristics are most important to balance.

Covariate Adaptive Randomization Uses

This design is particularly useful in medical trials. Let's say researchers are testing a new medication for high blood pressure. Participants might have different ages, weights, or pre-existing conditions that could affect the results.

Covariate Adaptive Randomization would make sure that each treatment group has a similar mix of these characteristics, making the results more reliable and easier to interpret.

In practical terms, this design is often seen in clinical trials for new drugs or therapies, but its principles are also applicable in fields like psychology, education, and social sciences.

For instance, in educational research, it might be used to ensure that classrooms being compared have similar distributions of students in terms of academic ability, socioeconomic status, and other factors.

Covariate Adaptive Randomization is like the wise elder of the group, ensuring that everyone has an equal opportunity to show their true capabilities, thereby making the collective results as reliable as possible.

20) Stepped Wedge Design

Let's now focus on the Stepped Wedge Design, a thoughtful and cautious member of the experimental design family.

Imagine you're trying out a new gardening technique, but you're not sure how well it will work. You decide to apply it to one section of your garden first, watch how it performs, and then gradually extend the technique to other sections. This way, you get to see its effects over time and across different conditions. That's basically how Stepped Wedge Design works.

In a Stepped Wedge Design, all participants or clusters start off in the control group, and then, at different times, they 'step' over to the intervention or treatment group. This creates a wedge-like pattern over time where more and more participants receive the treatment as the study progresses. It's like rolling out a new policy in phases, monitoring its impact at each stage before extending it to more people.

Stepped Wedge Design Pros

The Stepped Wedge Design offers several advantages. Firstly, it allows for the study of interventions that are expected to do more good than harm, which makes it ethically appealing.

Secondly, it's useful when resources are limited and it's not feasible to roll out a new treatment to everyone at once. Lastly, because everyone eventually receives the treatment, it can be easier to get buy-in from participants or organizations involved in the study.

Stepped Wedge Design Cons

However, this design can be complex to analyze because it has to account for both the time factor and the changing conditions in each 'step' of the wedge. And like any study where participants know they're receiving an intervention, there's the potential for the results to be influenced by the placebo effect or other biases.

Stepped Wedge Design Uses

This design is particularly useful in health and social care research. For instance, if a hospital wants to implement a new hygiene protocol, it might start in one department, assess its impact, and then roll it out to other departments over time. This allows the hospital to adjust and refine the new protocol based on real-world data before it's fully implemented.

In terms of applications, Stepped Wedge Designs are commonly used in public health initiatives, organizational changes in healthcare settings, and social policy trials. They are particularly useful in situations where an intervention is being rolled out gradually and it's important to understand its impacts at each stage.

21) Sequential Design

Next up is Sequential Design, the dynamic and flexible member of our experimental design family.

Imagine you're playing a video game where you can choose different paths. If you take one path and find a treasure chest, you might decide to continue in that direction. If you hit a dead end, you might backtrack and try a different route. Sequential Design operates in a similar fashion, allowing researchers to make decisions at different stages based on what they've learned so far.

In a Sequential Design, the experiment is broken down into smaller parts, or "sequences." After each sequence, researchers pause to look at the data they've collected. Based on those findings, they then decide whether to stop the experiment because they've got enough information, or to continue and perhaps even modify the next sequence.

Sequential Design Pros

This allows for a more efficient use of resources, as you're only continuing with the experiment if the data suggests it's worth doing so.

One of the great things about Sequential Design is its efficiency. Because you're making data-driven decisions along the way, you can often reach conclusions more quickly and with fewer resources.

Sequential Design Cons

However, it requires careful planning and expertise to ensure that these "stop or go" decisions are made correctly and without bias.

Sequential Design Uses

In terms of its applications, besides healthcare and medicine, Sequential Design is also popular in quality control in manufacturing, environmental monitoring, and financial modeling. In these areas, being able to make quick decisions based on incoming data can be a big advantage.

This design is often used in clinical trials involving new medications or treatments. For example, if early results show that a new drug has significant side effects, the trial can be stopped before more people are exposed to it.

On the flip side, if the drug is showing promising results, the trial might be expanded to include more participants or to extend the testing period.

Think of Sequential Design as the nimble athlete of experimental designs, capable of quick pivots and adjustments to reach the finish line in the most effective way possible. But just like an athlete needs a good coach, this design requires expert oversight to make sure it stays on the right track.

22) Field Experiments

Last but certainly not least, let's explore Field Experiments—the adventurers of the experimental design world.

Picture a scientist leaving the controlled environment of a lab to test a theory in the real world, like a biologist studying animals in their natural habitat or a social scientist observing people in a real community. These are Field Experiments, and they're all about getting out there and gathering data in real-world settings.

Field Experiments embrace the messiness of the real world, unlike laboratory experiments, where everything is controlled down to the smallest detail. This makes them both exciting and challenging.

Field Experiment Pros

On one hand, the results often give us a better understanding of how things work outside the lab.

While Field Experiments offer real-world relevance, they come with challenges like controlling for outside factors and the ethical considerations of intervening in people's lives without their knowledge.

Field Experiment Cons

On the other hand, the lack of control can make it harder to tell exactly what's causing what. Yet, despite these challenges, they remain a valuable tool for researchers who want to understand how theories play out in the real world.

Field Experiment Uses

Let's say a school wants to improve student performance. In a Field Experiment, they might change the school's daily schedule for one semester and keep track of how students perform compared to another school where the schedule remained the same.

Because the study is happening in a real school with real students, the results could be very useful for understanding how the change might work in other schools. But since it's the real world, lots of other factors—like changes in teachers or even the weather—could affect the results.

Field Experiments are widely used in economics, psychology, education, and public policy. For example, you might have heard of the famous "Broken Windows" experiment in the 1980s that looked at how small signs of disorder, like broken windows or graffiti, could encourage more serious crime in neighborhoods. This experiment had a big impact on how cities think about crime prevention.

From the foundational concepts of control groups and independent variables to the sophisticated layouts like Covariate Adaptive Randomization and Sequential Design, it's clear that the realm of experimental design is as varied as it is fascinating.

We've seen that each design has its own special talents, ideal for specific situations. Some designs, like the Classic Controlled Experiment, are like reliable old friends you can always count on.

Others, like Sequential Design, are flexible and adaptable, making quick changes based on what they learn. And let's not forget the adventurous Field Experiments, which take us out of the lab and into the real world to discover things we might not see otherwise.

Choosing the right experimental design is like picking the right tool for the job. The method you choose can make a big difference in how reliable your results are and how much people will trust what you've discovered. And as we've learned, there's a design to suit just about every question, every problem, and every curiosity.

So the next time you read about a new discovery in medicine, psychology, or any other field, you'll have a better understanding of the thought and planning that went into figuring things out. Experimental design is more than just a set of rules; it's a structured way to explore the unknown and answer questions that can change the world.

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Exploring the Art of Experimental Design: A Step-by-Step Guide for Students and Educators

Experimental design for students.

Experimental design is a key method used in subjects like biology, chemistry, physics, psychology, and social sciences. It helps us figure out how different factors affect what we're studying, whether it's plants, chemicals, physical laws, human behavior, or how society works. Basically, it's a way to set up experiments so we can test ideas, see what happens, and make sense of our results. It's super important for students and researchers who want to answer big questions in science and understand the world better. Experimental design skills can be applied in situations ranging from problem solving to data analysis; they are wide reaching and can frequently be applied outside the classroom. The teaching of these skills is a very important part of science education, but is often overlooked when focused on teaching the content. As science educators, we have all seen the benefits practical work has for student engagement and understanding. However, with the time constraints placed on the curriculum, the time needed for students to develop these experimental research design and investigative skills can get squeezed out. Too often they get a ‘recipe’ to follow, which doesn’t allow them to take ownership of their practical work. From a very young age, they start to think about the world around them. They ask questions then use observations and evidence to answer them. Students tend to have intelligent, interesting, and testable questions that they love to ask. As educators, we should be working towards encouraging these questions and in turn, nurturing this natural curiosity in the world around them.

Teaching the design of experiments and letting students develop their own questions and hypotheses takes time. These materials have been created to scaffold and structure the process to allow teachers to focus on improving the key ideas in experimental design. Allowing students to ask their own questions, write their own hypotheses, and plan and carry out their own investigations is a valuable experience for them. This will lead to students having more ownership of their work. When students carry out the experimental method for their own questions, they reflect on how scientists have historically come to understand how the universe works.

Take a look at the printer-friendly pages and worksheet templates below!

What are the Steps of Experimental Design?

Embarking on the journey of scientific discovery begins with mastering experimental design steps. This foundational process is essential for formulating experiments that yield reliable and insightful results, guiding researchers and students alike through the detailed planning, experimental research design, and execution of their studies. By leveraging an experimental design template, participants can ensure the integrity and validity of their findings. Whether it's through designing a scientific experiment or engaging in experimental design activities, the aim is to foster a deep understanding of the fundamentals: How should experiments be designed? What are the 7 experimental design steps? How can you design your own experiment?

This is an exploration of the seven key experimental method steps, experimental design ideas, and ways to integrate design of experiments. Student projects can benefit greatly from supplemental worksheets and we will also provide resources such as worksheets aimed at teaching experimental design effectively. Let’s dive into the essential stages that underpin the process of designing an experiment, equipping learners with the tools to explore their scientific curiosity.

1. Question

This is a key part of the scientific method and the experimental design process. Students enjoy coming up with questions. Formulating questions is a deep and meaningful activity that can give students ownership over their work. A great way of getting students to think of how to visualize their research question is using a mind map storyboard.

Ask students to think of any questions they want to answer about the universe or get them to think about questions they have about a particular topic. All questions are good questions, but some are easier to test than others.

2. Hypothesis

A hypothesis is known as an educated guess. A hypothesis should be a statement that can be tested scientifically. At the end of the experiment, look back to see whether the conclusion supports the hypothesis or not.

Forming good hypotheses can be challenging for students to grasp. It is important to remember that the hypothesis is not a research question, it is a testable statement . One way of forming a hypothesis is to form it as an “if... then...” statement. This certainly isn't the only or best way to form a hypothesis, but can be a very easy formula for students to use when first starting out.

An “if... then...” statement requires students to identify the variables first, and that may change the order in which they complete the stages of the visual organizer. After identifying the dependent and independent variables, the hypothesis then takes the form if [change in independent variable], then [change in dependent variable].

For example, if an experiment were looking for the effect of caffeine on reaction time, the independent variable would be amount of caffeine and the dependent variable would be reaction time. The “if, then” hypothesis could be: If you increase the amount of caffeine taken, then the reaction time will decrease.

3. Explanation of Hypothesis

What led you to this hypothesis? What is the scientific background behind your hypothesis? Depending on age and ability, students use their prior knowledge to explain why they have chosen their hypotheses, or alternatively, research using books or the internet. This could also be a good time to discuss with students what a reliable source is.

For example, students may reference previous studies showing the alertness effects of caffeine to explain why they hypothesize caffeine intake will reduce reaction time.

4. Prediction

The prediction is slightly different to the hypothesis. A hypothesis is a testable statement, whereas the prediction is more specific to the experiment. In the discovery of the structure of DNA, the hypothesis proposed that DNA has a helical structure. The prediction was that the X-ray diffraction pattern of DNA would be an X shape.

Students should formulate a prediction that is a specific, measurable outcome based on their hypothesis. Rather than just stating "caffeine will decrease reaction time," students could predict that "drinking 2 cans of soda (90mg caffeine) will reduce average reaction time by 50 milliseconds compared to drinking no caffeine."

5. Identification of Variables

Below is an example of a Discussion Storyboard that can be used to get your students talking about variables in experimental design.

The three types of variables you will need to discuss with your students are dependent, independent, and controlled variables. To keep this simple, refer to these as "what you are going to measure", "what you are going to change", and "what you are going to keep the same". With more advanced students, you should encourage them to use the correct vocabulary.

Dependent variables are what is measured or observed by the scientist. These measurements will often be repeated because repeated measurements makes your data more reliable.

The independent variables are variables that scientists decide to change to see what effect it has on the dependent variable. Only one is chosen because it would be difficult to figure out which variable is causing any change you observe.

Controlled variables are quantities or factors that scientists want to remain the same throughout the experiment. They are controlled to remain constant, so as to not affect the dependent variable. Controlling these allows scientists to see how the independent variable affects the dependent variable within the experimental group.

Use this example below in your lessons, or delete the answers and set it as an activity for students to complete on Storyboard That.

6. Risk Assessment

Ultimately this must be signed off on by a responsible adult, but it is important to get students to think about how they will keep themselves safe. In this part, students should identify potential risks and then explain how they are going to minimize risk. An activity to help students develop these skills is to get them to identify and manage risks in different situations. Using the storyboard below, get students to complete the second column of the T-chart by saying, "What is risk?", then explaining how they could manage that risk. This storyboard could also be projected for a class discussion.

7. Materials

In this section, students will list the materials they need for the experiments, including any safety equipment that they have highlighted as needing in the risk assessment section. This is a great time to talk to students about choosing tools that are suitable for the job. You are going to use a different tool to measure the width of a hair than to measure the width of a football field!

8. General Plan and Diagram

It is important to talk to students about reproducibility. They should write a procedure that would allow their experimental method to be reproduced easily by another scientist. The easiest and most concise way for students to do this is by making a numbered list of instructions. A useful activity here could be getting students to explain how to make a cup of tea or a sandwich. Act out the process, pointing out any steps they’ve missed.

For English Language Learners and students who struggle with written English, students can describe the steps in their experiment visually using Storyboard That.

Not every experiment will need a diagram, but some plans will be greatly improved by including one. Have students focus on producing clear and easy-to-understand diagrams that illustrate the experimental group.

For example, a procedure to test the effect of sunlight on plant growth utilizing completely randomized design could detail:

• Select 10 similar seedlings of the same age and variety
• Prepare 2 identical trays with the same soil mixture
• Place 5 plants in each tray; label one set "sunlight" and one set "shade"
• Position sunlight tray by a south-facing window, and shade tray in a dark closet
• Water both trays with 50 mL water every 2 days
• After 3 weeks, remove plants and measure heights in cm

9. Carry Out Experiment

Once their procedure is approved, students should carefully carry out their planned experiment, following their written instructions. As data is collected, students should organize the raw results in tables, graphs, photos or drawings. This creates clear documentation for analyzing trends.

Some best practices for data collection include:

• Record quantitative data numerically with units
• Note qualitative observations with detailed descriptions
• Capture set up through illustrations or photos
• Write observations of unexpected events
• Identify data outliers and sources of error

For example, in the plant growth experiment, students could record:

They would also describe observations like leaf color change or directional bending visually or in writing.

It is crucial that students practice safe science procedures. Adult supervision is required for experimentation, along with proper risk assessment.

Well-documented data collection allows for deeper analysis after experiment completion to determine whether hypotheses and predictions were supported.

Completed Examples

Resources and Experimental Design Examples

Using visual organizers is an effective way to get your students working as scientists in the classroom.

There are many ways to use these investigation planning tools to scaffold and structure students' work while they are working as scientists. Students can complete the planning stage on Storyboard That using the text boxes and diagrams, or you could print them off and have students complete them by hand. Another great way to use them is to project the planning sheet onto an interactive whiteboard and work through how to complete the planning materials as a group. Project it onto a screen and have students write their answers on sticky notes and put their ideas in the correct section of the planning document.

Very young learners can still start to think as scientists! They have loads of questions about the world around them and you can start to make a note of these in a mind map. Sometimes you can even start to ‘investigate’ these questions through play.

The foundation resource is intended for elementary students or students who need more support. It is designed to follow exactly the same process as the higher resources, but made slightly easier. The key difference between the two resources are the details that students are required to think about and the technical vocabulary used. For example, it is important that students identify variables when they are designing their investigations. In the higher version, students not only have to identify the variables, but make other comments, such as how they are going to measure the dependent variable or utilizing completely randomized design. As well as the difference in scaffolding between the two levels of resources, you may want to further differentiate by how the learners are supported by teachers and assistants in the room.

Students could also be encouraged to make their experimental plan easier to understand by using graphics, and this could also be used to support ELLs.

Students need to be assessed on their science inquiry skills alongside the assessment of their knowledge. Not only will that let students focus on developing their skills, but will also allow them to use their assessment information in a way that will help them improve their science skills. Using Quick Rubric , you can create a quick and easy assessment framework and share it with students so they know how to succeed at every stage. As well as providing formative assessment which will drive learning, this can also be used to assess student work at the end of an investigation and set targets for when they next attempt to plan their own investigation. The rubrics have been written in a way to allow students to access them easily. This way they can be shared with students as they are working through the planning process so students know what a good experimental design looks like.

Printable Resources

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Related Activities

Additional Worksheets

If you're looking to add additional projects or continue to customize worksheets, take a look at several template pages we've compiled for you below. Each worksheet can be copied and tailored to your projects or students! Students can also be encouraged to create their own if they want to try organizing information in an easy to understand way.

• Lab Worksheets
• Discussion Worksheets
• Checklist Worksheets

Related Resources

• Scientific Method Steps
• Science Discussion Storyboards
• Developing Critical Thinking Skills

How to Teach Students the Design of Experiments

Encourage questioning and curiosity.

Foster a culture of inquiry by encouraging students to ask questions about the world around them.

Formulate testable hypotheses

Teach students how to develop hypotheses that can be scientifically tested. Help them understand the difference between a hypothesis and a question.

Provide scientific background

Help students understand the scientific principles and concepts relevant to their hypotheses. Encourage them to draw on prior knowledge or conduct research to support their hypotheses.

Identify variables

Teach students about the three types of variables (dependent, independent, and controlled) and how they relate to experimental design. Emphasize the importance of controlling variables and measuring the dependent variable accurately.

Plan and diagram the experiment

Guide students in developing a clear and reproducible experimental procedure. Encourage them to create a step-by-step plan or use visual diagrams to illustrate the process.

Carry out the experiment and analyze data

Support students as they conduct the experiment according to their plan. Guide them in collecting data in a meaningful and organized manner. Assist them in analyzing the data and drawing conclusions based on their findings.

Frequently Asked Questions about Experimental Design for Students

What are some common experimental design tools and techniques that students can use.

Common experimental design tools and techniques that students can use include random assignment, control groups, blinding, replication, and statistical analysis. Students can also use observational studies, surveys, and experiments with natural or quasi-experimental designs. They can also use data visualization tools to analyze and present their results.

How can experimental design help students develop critical thinking skills?

Experimental design helps students develop critical thinking skills by encouraging them to think systematically and logically about scientific problems. It requires students to analyze data, identify patterns, and draw conclusions based on evidence. It also helps students to develop problem-solving skills by providing opportunities to design and conduct experiments to test hypotheses.

How can experimental design be used to address real-world problems?

Experimental design can be used to address real-world problems by identifying variables that contribute to a particular problem and testing interventions to see if they are effective in addressing the problem. For example, experimental design can be used to test the effectiveness of new medical treatments or to evaluate the impact of social interventions on reducing poverty or improving educational outcomes.

What are some common experimental design pitfalls that students should avoid?

Common experimental design pitfalls that students should avoid include failing to control variables, using biased samples, relying on anecdotal evidence, and failing to measure dependent variables accurately. Students should also be aware of ethical considerations when conducting experiments, such as obtaining informed consent and protecting the privacy of research subjects.

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Experiments: From Idea to Design

For researchers in the natural sciences who want to develop their experimental design skills

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About this course

‘Experiments: From Idea to Design’ equips you with the right tools to help develop, plan and refine robust, impactful experiments. You will cover all the core concepts of experimental design and discover strategies to complete the full process of developing a research motivation, formulating hypotheses, assembling an experimental plan and utilising it.

What you’ll learn

• The benefits of honing your experimental design skills before embarking on full-scale experiments
• How to develop research motivations, identify assumptions and formulate hypotheses
• How to select the precise methods, tools, techniques and protocols you need to answer your research question
• How to refine and make use of your experimental design

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Distinguished Professor of Chemical and Biomolecular Engineering and Senior Vice Provost (Faculty and Institutional Development), National University of Singapore (NUS)

Massimiliano Di Ventra

Professor of Physics, University of California, San Diego

Allison Doerr

Chief Editor, Nature Methods

Oliver Graydon

Chief Editor,  Nature Photonics

Ülo Niinemets

Professor of Plant Physiology and Head of the Chair, Estonian University of Life Sciences

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Melanie Clyne

Chief Editor,  Nature Protocols

David Lapola

Research Scientist, University of Campinas

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Keyboard Shortcuts

Lesson 1: introduction to design of experiments, overview section  .

In this course we will pretty much cover the textbook - all of the concepts and designs included. I think we will have plenty of examples to look at and experience to draw from.

Please note: the main topics listed in the syllabus follow the chapters in the book.

A word of advice regarding the analyses. The prerequisite for this course is STAT 501 - Regression Methods and STAT 502 - Analysis of Variance . However, the focus of the course is on the design and not on the analysis. Thus, one can successfully complete this course without these prerequisites, with just STAT 500 - Applied Statistics for instance, but it will require much more work, and for the analysis less appreciation of the subtleties involved. You might say it is more conceptual than it is math oriented.

  Text Reference: Montgomery, D. C. (2019). Design and Analysis of Experiments , 10th Edition, John Wiley & Sons. ISBN 978-1-119-59340-9

What is the Scientific Method? Section  

Do you remember learning about this back in high school or junior high even? What were those steps again?

Decide what phenomenon you wish to investigate. Specify how you can manipulate the factor and hold all other conditions fixed, to insure that these extraneous conditions aren't influencing the response you plan to measure.

Then measure your chosen response variable at several (at least two) settings of the factor under study. If changing the factor causes the phenomenon to change, then you conclude that there is indeed a cause-and-effect relationship at work.

How many factors are involved when you do an experiment? Some say two - perhaps this is a comparative experiment? Perhaps there is a treatment group and a control group? If you have a treatment group and a control group then, in this case, you probably only have one factor with two levels.

How many of you have baked a cake? What are the factors involved to ensure a successful cake? Factors might include preheating the oven, baking time, ingredients, amount of moisture, baking temperature, etc.-- what else? You probably follow a recipe so there are many additional factors that control the ingredients - i.e., a mixture. In other words, someone did the experiment in advance! What parts of the recipe did they vary to make the recipe a success? Probably many factors, temperature and moisture, various ratios of ingredients, and presence or absence of many additives.  Now, should one keep all the factors involved in the experiment at a constant level and just vary one to see what would happen?  This is a strategy that works but is not very efficient.  This is one of the concepts that we will address in this course.

• understand the issues and principles of Design of Experiments (DOE),
• understand experimentation is a process,
• list the guidelines for designing experiments, and
• recognize the key historical figures in DOE.

How To Design a Science Fair Experiment

Design a Science Fair Experiment Using the Scientific Method

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A good science fair experiment applies the scientific method to answer a question or test an effect. Follow these steps to design an experiment that follows the approved procedure for science fair projects.

State an Objective

Science fair projects start with a purpose or objective. Why are you studying this? What do you hope to learn? What makes this topic interesting? An objective is a brief statement of the goal of an experiment, which you can use to help narrow down choices for a hypothesis.

Propose a Testable Hypothesis

The hardest part of experimental design may be the first step, which is deciding what to test and proposing a hypothesis you can use to build an experiment.

You could state the hypothesis as an if-then statement. Example: "If plants are not given light, then they will not grow."

You could state a null or no-difference hypothesis, which is an easy form to test. Example: There is no difference in the size of beans soaked in water compared with beans soaked in saltwater.

The key to formulating a good science fair hypothesis is to make sure you have the ability to test it, record data, and draw a conclusion. Compare these two hypotheses and decide which you could test:

Cupcakes sprinkled with colored sugar are better than plain frosted cupcakes.

People are more likely to choose cupcakes sprinkled with colored sugar than plain frosted cupcakes.

Once you have an idea for an experiment, it often helps to write out several different versions of a hypothesis and select the one that works best for you.

See Hypothesis Examples

Identify the Independent, Dependent, and Control Variable

To draw a valid conclusion from your experiment, you ideally want to test the effect of changing one factor, while holding all other factors constant or unchanged. There are several possible variables in an experiment, but be sure to identify the big three: independent , dependent , and control variables.

The independent variable is the one you manipulate or change to test its effect on the dependent variable. Controlled variables are other factors in your experiment you try to control or hold constant.

For example, let's say your hypothesis is: Duration of daylight has no effect on how long a cat sleeps. Your independent variable is duration of daylight (how many hours of daylight the cat sees). The dependent variable is how long the cat sleeps per day. Controlled variables might include amount of exercise and cat food supplied to the cat, how often it is disturbed, whether or not other cats are present, the approximate age of cats that are tested, etc.

Perform Enough Tests

Consider an experiment with the hypothesis: If you toss a coin, there is an equal chance of it coming up heads or tails. That is a nice, testable hypothesis, but you can't draw any sort of valid conclusion from a single coin toss. Neither are you likely to get enough data from 2-3 coin tosses, or even 10. It's important to have a large enough sample size that your experiment isn't overly influenced by randomness. Sometimes this means you need to perform a test multiple times on a single subject or small set of subjects. In other cases, you may want to gather data from a large, representative sample of population.

Gather the Right Data

There are two main types of data: qualitative and quantitative data. Qualitative data describes a quality, such as red/green, more/less, yes/no. Quantitative data is recorded as a number. If you can, gather quantitative data because it's much easier to analyze using mathematical tests.

Tabulate or Graph the Results

Once you have recorded your data, report it in a table and/or graph. This visual representation of the data makes it easier for you to see patterns or trends and makes your science fair project more appealing to other students, teachers, and judges.

Test the Hypothesis

Was the hypothesis accepted or rejected? Once you make this determination, ask yourself whether you met the objective of the experiment or whether further study is needed. Sometimes an experiment doesn't work out the way you expect. You may accept the experiment or decide to conduct a new experiment, based on what you learned.

Draw a Conclusion

Based on the experience you gained from the experiment and whether you accepted or rejected the hypothesis, you should be able to draw some conclusions about your subject. You should state these in your report.

• Examples of Independent and Dependent Variables
• Null Hypothesis Examples
• Difference Between Independent and Dependent Variables
• The 10 Most Important Lab Safety Rules
• Six Steps of the Scientific Method
• What Is an Experiment? Definition and Design
• What Is a Testable Hypothesis?
• What Are the Elements of a Good Hypothesis?
• Scientific Method Flow Chart
• Scientific Method Vocabulary Terms
• How to Do a Science Fair Project
• How to Select a Science Fair Project Topic
• What Is a Hypothesis? (Science)
• 5 Types of Science Fair Projects
• Understanding Simple vs Controlled Experiments

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• A Quick Guide to Experimental Design | 5 Steps & Examples

A Quick Guide to Experimental Design | 5 Steps & Examples

Published on 11 April 2022 by Rebecca Bevans . Revised on 5 December 2022.

Experiments are used to study causal relationships . You manipulate one or more independent variables and measure their effect on one or more dependent variables.

Experimental design means creating a set of procedures to systematically test a hypothesis . A good experimental design requires a strong understanding of the system you are studying. 

There are five key steps in designing an experiment:

• Consider your variables and how they are related
• Write a specific, testable hypothesis
• Design experimental treatments to manipulate your independent variable
• Assign subjects to groups, either between-subjects or within-subjects
• Plan how you will measure your dependent variable

For valid conclusions, you also need to select a representative sample and control any  extraneous variables that might influence your results. If if random assignment of participants to control and treatment groups is impossible, unethical, or highly difficult, consider an observational study instead.

Table of contents

Step 1: define your variables, step 2: write your hypothesis, step 3: design your experimental treatments, step 4: assign your subjects to treatment groups, step 5: measure your dependent variable, frequently asked questions about experimental design.

You should begin with a specific research question . We will work with two research question examples, one from health sciences and one from ecology:

To translate your research question into an experimental hypothesis, you need to define the main variables and make predictions about how they are related.

Start by simply listing the independent and dependent variables .

Then you need to think about possible extraneous and confounding variables and consider how you might control  them in your experiment.

Finally, you can put these variables together into a diagram. Use arrows to show the possible relationships between variables and include signs to show the expected direction of the relationships.

Here we predict that increasing temperature will increase soil respiration and decrease soil moisture, while decreasing soil moisture will lead to decreased soil respiration.

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Now that you have a strong conceptual understanding of the system you are studying, you should be able to write a specific, testable hypothesis that addresses your research question.

The next steps will describe how to design a controlled experiment . In a controlled experiment, you must be able to:

• Systematically and precisely manipulate the independent variable(s).
• Precisely measure the dependent variable(s).
• Control any potential confounding variables.

If your study system doesn’t match these criteria, there are other types of research you can use to answer your research question.

How you manipulate the independent variable can affect the experiment’s external validity – that is, the extent to which the results can be generalised and applied to the broader world.

First, you may need to decide how widely to vary your independent variable.

• just slightly above the natural range for your study region.
• over a wider range of temperatures to mimic future warming.
• over an extreme range that is beyond any possible natural variation.

Second, you may need to choose how finely to vary your independent variable. Sometimes this choice is made for you by your experimental system, but often you will need to decide, and this will affect how much you can infer from your results.

• a categorical variable : either as binary (yes/no) or as levels of a factor (no phone use, low phone use, high phone use).
• a continuous variable (minutes of phone use measured every night).

How you apply your experimental treatments to your test subjects is crucial for obtaining valid and reliable results.

First, you need to consider the study size : how many individuals will be included in the experiment? In general, the more subjects you include, the greater your experiment’s statistical power , which determines how much confidence you can have in your results.

Then you need to randomly assign your subjects to treatment groups . Each group receives a different level of the treatment (e.g. no phone use, low phone use, high phone use).

You should also include a control group , which receives no treatment. The control group tells us what would have happened to your test subjects without any experimental intervention.

When assigning your subjects to groups, there are two main choices you need to make:

• A completely randomised design vs a randomised block design .
• A between-subjects design vs a within-subjects design .

Randomisation

An experiment can be completely randomised or randomised within blocks (aka strata):

• In a completely randomised design , every subject is assigned to a treatment group at random.
• In a randomised block design (aka stratified random design), subjects are first grouped according to a characteristic they share, and then randomly assigned to treatments within those groups.

Sometimes randomisation isn’t practical or ethical , so researchers create partially-random or even non-random designs. An experimental design where treatments aren’t randomly assigned is called a quasi-experimental design .

Between-subjects vs within-subjects

In a between-subjects design (also known as an independent measures design or classic ANOVA design), individuals receive only one of the possible levels of an experimental treatment.

In medical or social research, you might also use matched pairs within your between-subjects design to make sure that each treatment group contains the same variety of test subjects in the same proportions.

In a within-subjects design (also known as a repeated measures design), every individual receives each of the experimental treatments consecutively, and their responses to each treatment are measured.

Within-subjects or repeated measures can also refer to an experimental design where an effect emerges over time, and individual responses are measured over time in order to measure this effect as it emerges.

Counterbalancing (randomising or reversing the order of treatments among subjects) is often used in within-subjects designs to ensure that the order of treatment application doesn’t influence the results of the experiment.

Finally, you need to decide how you’ll collect data on your dependent variable outcomes. You should aim for reliable and valid measurements that minimise bias or error.

Some variables, like temperature, can be objectively measured with scientific instruments. Others may need to be operationalised to turn them into measurable observations.

• Ask participants to record what time they go to sleep and get up each day.
• Ask participants to wear a sleep tracker.

How precisely you measure your dependent variable also affects the kinds of statistical analysis you can use on your data.

Experiments are always context-dependent, and a good experimental design will take into account all of the unique considerations of your study system to produce information that is both valid and relevant to your research question.

Experimental designs are a set of procedures that you plan in order to examine the relationship between variables that interest you.

To design a successful experiment, first identify:

• A testable hypothesis
• One or more independent variables that you will manipulate
• One or more dependent variables that you will measure

When designing the experiment, first decide:

• How your variable(s) will be manipulated
• How you will control for any potential confounding or lurking variables
• How many subjects you will include
• How you will assign treatments to your subjects

The key difference between observational studies and experiments is that, done correctly, an observational study will never influence the responses or behaviours of participants. Experimental designs will have a treatment condition applied to at least a portion of participants.

A confounding variable , also called a confounder or confounding factor, is a third variable in a study examining a potential cause-and-effect relationship.

A confounding variable is related to both the supposed cause and the supposed effect of the study. It can be difficult to separate the true effect of the independent variable from the effect of the confounding variable.

In your research design , it’s important to identify potential confounding variables and plan how you will reduce their impact.

In a between-subjects design , every participant experiences only one condition, and researchers assess group differences between participants in various conditions.

In a within-subjects design , each participant experiences all conditions, and researchers test the same participants repeatedly for differences between conditions.

The word ‘between’ means that you’re comparing different conditions between groups, while the word ‘within’ means you’re comparing different conditions within the same group.

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Course: biology archive   >   unit 1.

• The scientific method
• Controlled experiments

The scientific method and experimental design

• (Choice A)   The facts collected from an experiment are written in the form of a hypothesis. A The facts collected from an experiment are written in the form of a hypothesis.
• (Choice B)   A hypothesis is the correct answer to a scientific question. B A hypothesis is the correct answer to a scientific question.
• (Choice C)   A hypothesis is a possible, testable explanation for a scientific question. C A hypothesis is a possible, testable explanation for a scientific question.
• (Choice D)   A hypothesis is the process of making careful observations. D A hypothesis is the process of making careful observations.

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Before you step into the lab to do an experiment, you have a long list of questions: How do I design an experiment that will give a clear answer to my question? What model system should I use? What are my controls? What’s an ideal sample size? How can I tell if the experiment worked?

It is overwhelming and easy to feel lost, especially with no guide in sight.

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72 Easy Science Experiments Using Materials You Already Have On Hand

Because science doesn’t have to be complicated.

If there is one thing that is guaranteed to get your students excited, it’s a good science experiment! While some experiments require expensive lab equipment or dangerous chemicals, there are plenty of cool projects you can do with regular household items. We’ve rounded up a big collection of easy science experiments that anybody can try, and kids are going to love them!

Easy Chemistry Science Experiments

Easy physics science experiments, easy biology and environmental science experiments, easy engineering experiments and stem challenges.

1. Taste the Rainbow

Teach your students about diffusion while creating a beautiful and tasty rainbow! Tip: Have extra Skittles on hand so your class can eat a few!

Learn more: Skittles Diffusion

2. Crystallize sweet treats

Crystal science experiments teach kids about supersaturated solutions. This one is easy to do at home, and the results are absolutely delicious!

Learn more: Candy Crystals

3. Make a volcano erupt

This classic experiment demonstrates a chemical reaction between baking soda (sodium bicarbonate) and vinegar (acetic acid), which produces carbon dioxide gas, water, and sodium acetate.

Learn more: Best Volcano Experiments

4. Make elephant toothpaste

This fun project uses yeast and a hydrogen peroxide solution to create overflowing “elephant toothpaste.” Tip: Add an extra fun layer by having kids create toothpaste wrappers for plastic bottles.

5. Blow the biggest bubbles you can

Add a few simple ingredients to dish soap solution to create the largest bubbles you’ve ever seen! Kids learn about surface tension as they engineer these bubble-blowing wands.

Learn more: Giant Soap Bubbles

6. Demonstrate the “magic” leakproof bag

All you need is a zip-top plastic bag, sharp pencils, and water to blow your kids’ minds. Once they’re suitably impressed, teach them how the “trick” works by explaining the chemistry of polymers.

Learn more: Leakproof Bag

7. Use apple slices to learn about oxidation

Have students make predictions about what will happen to apple slices when immersed in different liquids, then put those predictions to the test. Have them record their observations.

Learn more: Apple Oxidation

8. Float a marker man

Their eyes will pop out of their heads when you “levitate” a stick figure right off the table! This experiment works due to the insolubility of dry-erase marker ink in water, combined with the lighter density of the ink.

Learn more: Floating Marker Man

9. Discover density with hot and cold water

There are a lot of easy science experiments you can do with density. This one is extremely simple, involving only hot and cold water and food coloring, but the visuals make it appealing and fun.

Learn more: Layered Water

10. Layer more liquids

This density demo is a little more complicated, but the effects are spectacular. Slowly layer liquids like honey, dish soap, water, and rubbing alcohol in a glass. Kids will be amazed when the liquids float one on top of the other like magic (except it is really science).

Learn more: Layered Liquids

11. Grow a carbon sugar snake

Easy science experiments can still have impressive results! This eye-popping chemical reaction demonstration only requires simple supplies like sugar, baking soda, and sand.

Learn more: Carbon Sugar Snake

12. Mix up some slime

Tell kids you’re going to make slime at home, and watch their eyes light up! There are a variety of ways to make slime, so try a few different recipes to find the one you like best.

13. Make homemade bouncy balls

These homemade bouncy balls are easy to make since all you need is glue, food coloring, borax powder, cornstarch, and warm water. You’ll want to store them inside a container like a plastic egg because they will flatten out over time.

Learn more: Make Your Own Bouncy Balls

14. Create eggshell chalk

Eggshells contain calcium, the same material that makes chalk. Grind them up and mix them with flour, water, and food coloring to make your very own sidewalk chalk.

Learn more: Eggshell Chalk

15. Make naked eggs

This is so cool! Use vinegar to dissolve the calcium carbonate in an eggshell to discover the membrane underneath that holds the egg together. Then, use the “naked” egg for another easy science experiment that demonstrates osmosis .

Learn more: Naked Egg Experiment

16. Turn milk into plastic

This sounds a lot more complicated than it is, but don’t be afraid to give it a try. Use simple kitchen supplies to create plastic polymers from plain old milk. Sculpt them into cool shapes when you’re done!

17. Test pH using cabbage

Teach kids about acids and bases without needing pH test strips! Simply boil some red cabbage and use the resulting water to test various substances—acids turn red and bases turn green.

Learn more: Cabbage pH

18. Clean some old coins

Use common household items to make old oxidized coins clean and shiny again in this simple chemistry experiment. Ask kids to predict (hypothesize) which will work best, then expand the learning by doing some research to explain the results.

Learn more: Cleaning Coins

19. Pull an egg into a bottle

This classic easy science experiment never fails to delight. Use the power of air pressure to suck a hard-boiled egg into a jar, no hands required.

Learn more: Egg in a Bottle

20. Blow up a balloon (without blowing)

Chances are good you probably did easy science experiments like this when you were in school. The baking soda and vinegar balloon experiment demonstrates the reactions between acids and bases when you fill a bottle with vinegar and a balloon with baking soda.

21 Assemble a DIY lava lamp

This 1970s trend is back—as an easy science experiment! This activity combines acid-base reactions with density for a totally groovy result.

22. Explore how sugary drinks affect teeth

The calcium content of eggshells makes them a great stand-in for teeth. Use eggs to explore how soda and juice can stain teeth and wear down the enamel. Expand your learning by trying different toothpaste-and-toothbrush combinations to see how effective they are.

Learn more: Sugar and Teeth Experiment

23. Mummify a hot dog

If your kids are fascinated by the Egyptians, they’ll love learning to mummify a hot dog! No need for canopic jars , just grab some baking soda and get started.

24. Extinguish flames with carbon dioxide

This is a fiery twist on acid-base experiments. Light a candle and talk about what fire needs in order to survive. Then, create an acid-base reaction and “pour” the carbon dioxide to extinguish the flame. The CO2 gas acts like a liquid, suffocating the fire.

25. Send secret messages with invisible ink

Turn your kids into secret agents! Write messages with a paintbrush dipped in lemon juice, then hold the paper over a heat source and watch the invisible become visible as oxidation goes to work.

Learn more: Invisible Ink

26. Create dancing popcorn

This is a fun version of the classic baking soda and vinegar experiment, perfect for the younger crowd. The bubbly mixture causes popcorn to dance around in the water.

27. Shoot a soda geyser sky-high

You’ve always wondered if this really works, so it’s time to find out for yourself! Kids will marvel at the chemical reaction that sends diet soda shooting high in the air when Mentos are added.

Learn more: Soda Explosion

28. Send a teabag flying

Hot air rises, and this experiment can prove it! You’ll want to supervise kids with fire, of course. For more safety, try this one outside.

Learn more: Flying Tea Bags

29. Create magic milk

This fun and easy science experiment demonstrates principles related to surface tension, molecular interactions, and fluid dynamics.

Learn more: Magic Milk Experiment

30. Watch the water rise

Learn about Charles’s Law with this simple experiment. As the candle burns, using up oxygen and heating the air in the glass, the water rises as if by magic.

Learn more: Rising Water

31. Learn about capillary action

Kids will be amazed as they watch the colored water move from glass to glass, and you’ll love the easy and inexpensive setup. Gather some water, paper towels, and food coloring to teach the scientific magic of capillary action.

Learn more: Capillary Action

32. Give a balloon a beard

Equally educational and fun, this experiment will teach kids about static electricity using everyday materials. Kids will undoubtedly get a kick out of creating beards on their balloon person!

Learn more: Static Electricity

33. Find your way with a DIY compass

Here’s an old classic that never fails to impress. Magnetize a needle, float it on the water’s surface, and it will always point north.

Learn more: DIY Compass

34. Crush a can using air pressure

Sure, it’s easy to crush a soda can with your bare hands, but what if you could do it without touching it at all? That’s the power of air pressure!

35. Tell time using the sun

While people use clocks or even phones to tell time today, there was a time when a sundial was the best means to do that. Kids will certainly get a kick out of creating their own sundials using everyday materials like cardboard and pencils.

Learn more: Make Your Own Sundial

36. Launch a balloon rocket

Grab balloons, string, straws, and tape, and launch rockets to learn about the laws of motion.

37. Make sparks with steel wool

All you need is steel wool and a 9-volt battery to perform this science demo that’s bound to make their eyes light up! Kids learn about chain reactions, chemical changes, and more.

Learn more: Steel Wool Electricity

38. Levitate a Ping-Pong ball

Kids will get a kick out of this experiment, which is really all about Bernoulli’s principle. You only need plastic bottles, bendy straws, and Ping-Pong balls to make the science magic happen.

39. Whip up a tornado in a bottle

There are plenty of versions of this classic experiment out there, but we love this one because it sparkles! Kids learn about a vortex and what it takes to create one.

Learn more: Tornado in a Bottle

40. Monitor air pressure with a DIY barometer

This simple but effective DIY science project teaches kids about air pressure and meteorology. They’ll have fun tracking and predicting the weather with their very own barometer.

Learn more: DIY Barometer

41. Peer through an ice magnifying glass

Students will certainly get a thrill out of seeing how an everyday object like a piece of ice can be used as a magnifying glass. Be sure to use purified or distilled water since tap water will have impurities in it that will cause distortion.

Learn more: Ice Magnifying Glass

42. String up some sticky ice

Can you lift an ice cube using just a piece of string? This quick experiment teaches you how. Use a little salt to melt the ice and then refreeze the ice with the string attached.

Learn more: Sticky Ice

43. “Flip” a drawing with water

Light refraction causes some really cool effects, and there are multiple easy science experiments you can do with it. This one uses refraction to “flip” a drawing; you can also try the famous “disappearing penny” trick .

Learn more: Light Refraction With Water

44. Color some flowers

We love how simple this project is to re-create since all you’ll need are some white carnations, food coloring, glasses, and water. The end result is just so beautiful!

45. Use glitter to fight germs

Everyone knows that glitter is just like germs—it gets everywhere and is so hard to get rid of! Use that to your advantage and show kids how soap fights glitter and germs.

Learn more: Glitter Germs

46. Re-create the water cycle in a bag

You can do so many easy science experiments with a simple zip-top bag. Fill one partway with water and set it on a sunny windowsill to see how the water evaporates up and eventually “rains” down.

Learn more: Water Cycle

47. Learn about plant transpiration

Your backyard is a terrific place for easy science experiments. Grab a plastic bag and rubber band to learn how plants get rid of excess water they don’t need, a process known as transpiration.

Learn more: Plant Transpiration

48. Clean up an oil spill

Before conducting this experiment, teach your students about engineers who solve environmental problems like oil spills. Then, have your students use provided materials to clean the oil spill from their oceans.

Learn more: Oil Spill

49. Construct a pair of model lungs

Kids get a better understanding of the respiratory system when they build model lungs using a plastic water bottle and some balloons. You can modify the experiment to demonstrate the effects of smoking too.

Learn more: Model Lungs

50. Experiment with limestone rocks

Kids  love to collect rocks, and there are plenty of easy science experiments you can do with them. In this one, pour vinegar over a rock to see if it bubbles. If it does, you’ve found limestone!

Learn more: Limestone Experiments

51. Turn a bottle into a rain gauge

All you need is a plastic bottle, a ruler, and a permanent marker to make your own rain gauge. Monitor your measurements and see how they stack up against meteorology reports in your area.

Learn more: DIY Rain Gauge

52. Build up towel mountains

This clever demonstration helps kids understand how some landforms are created. Use layers of towels to represent rock layers and boxes for continents. Then pu-u-u-sh and see what happens!

Learn more: Towel Mountains

53. Take a play dough core sample

Learn about the layers of the earth by building them out of Play-Doh, then take a core sample with a straw. ( Love Play-Doh? Get more learning ideas here. )

Learn more: Play Dough Core Sampling

54. Project the stars on your ceiling

Use the video lesson in the link below to learn why stars are only visible at night. Then create a DIY star projector to explore the concept hands-on.

Learn more: DIY Star Projector

55. Make it rain

Use shaving cream and food coloring to simulate clouds and rain. This is an easy science experiment little ones will beg to do over and over.

Learn more: Shaving Cream Rain

56. Blow up your fingerprint

This is such a cool (and easy!) way to look at fingerprint patterns. Inflate a balloon a bit, use some ink to put a fingerprint on it, then blow it up big to see your fingerprint in detail.

57. Snack on a DNA model

Twizzlers, gumdrops, and a few toothpicks are all you need to make this super-fun (and yummy!) DNA model.

Learn more: Edible DNA Model

58. Dissect a flower

Take a nature walk and find a flower or two. Then bring them home and take them apart to discover all the different parts of flowers.

59. Craft smartphone speakers

No Bluetooth speaker? No problem! Put together your own from paper cups and toilet paper tubes.

Learn more: Smartphone Speakers

60. Race a balloon-powered car

Kids will be amazed when they learn they can put together this awesome racer using cardboard and bottle-cap wheels. The balloon-powered “engine” is so much fun too.

Learn more: Balloon-Powered Car

61. Build a Ferris wheel

You’ve probably ridden on a Ferris wheel, but can you build one? Stock up on wood craft sticks and find out! Play around with different designs to see which one works best.

Learn more: Craft Stick Ferris Wheel

62. Design a phone stand

There are lots of ways to craft a DIY phone stand, which makes this a perfect creative-thinking STEM challenge.

63. Conduct an egg drop

Put all their engineering skills to the test with an egg drop! Challenge kids to build a container from stuff they find around the house that will protect an egg from a long fall (this is especially fun to do from upper-story windows).

Learn more: Egg Drop Challenge Ideas

64. Engineer a drinking-straw roller coaster

STEM challenges are always a hit with kids. We love this one, which only requires basic supplies like drinking straws.

Learn more: Straw Roller Coaster

65. Build a solar oven

Explore the power of the sun when you build your own solar ovens and use them to cook some yummy treats. This experiment takes a little more time and effort, but the results are always impressive. The link below has complete instructions.

Learn more: Solar Oven

66. Build a Da Vinci bridge

There are plenty of bridge-building experiments out there, but this one is unique. It’s inspired by Leonardo da Vinci’s 500-year-old self-supporting wooden bridge. Learn how to build it at the link, and expand your learning by exploring more about Da Vinci himself.

Learn more: Da Vinci Bridge

67. Step through an index card

This is one easy science experiment that never fails to astonish. With carefully placed scissor cuts on an index card, you can make a loop large enough to fit a (small) human body through! Kids will be wowed as they learn about surface area.

68. Stand on a pile of paper cups

Combine physics and engineering and challenge kids to create a paper cup structure that can support their weight. This is a cool project for aspiring architects.

Learn more: Paper Cup Stack

69. Test out parachutes

Gather a variety of materials (try tissues, handkerchiefs, plastic bags, etc.) and see which ones make the best parachutes. You can also find out how they’re affected by windy days or find out which ones work in the rain.

Learn more: Parachute Drop

70. Recycle newspapers into an engineering challenge

It’s amazing how a stack of newspapers can spark such creative engineering. Challenge kids to build a tower, support a book, or even build a chair using only newspaper and tape!

Learn more: Newspaper STEM Challenge

71. Use rubber bands to sound out acoustics

Explore the ways that sound waves are affected by what’s around them using a simple rubber band “guitar.” (Kids absolutely love playing with these!)

Learn more: Rubber Band Guitar

72. Assemble a better umbrella

Challenge students to engineer the best possible umbrella from various household supplies. Encourage them to plan, draw blueprints, and test their creations using the scientific method.

Learn more: Umbrella STEM Challenge

Plus, sign up for our newsletters to get all the latest learning ideas straight to your inbox.

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Home » Experimental Design – Types, Methods, Guide

Experimental Design – Types, Methods, Guide

Table of Contents

Experimental Design

Experimental design is a process of planning and conducting scientific experiments to investigate a hypothesis or research question. It involves carefully designing an experiment that can test the hypothesis, and controlling for other variables that may influence the results.

Experimental design typically includes identifying the variables that will be manipulated or measured, defining the sample or population to be studied, selecting an appropriate method of sampling, choosing a method for data collection and analysis, and determining the appropriate statistical tests to use.

Types of Experimental Design

Here are the different types of experimental design:

Completely Randomized Design

In this design, participants are randomly assigned to one of two or more groups, and each group is exposed to a different treatment or condition.

Randomized Block Design

This design involves dividing participants into blocks based on a specific characteristic, such as age or gender, and then randomly assigning participants within each block to one of two or more treatment groups.

Factorial Design

In a factorial design, participants are randomly assigned to one of several groups, each of which receives a different combination of two or more independent variables.

Repeated Measures Design

In this design, each participant is exposed to all of the different treatments or conditions, either in a random order or in a predetermined order.

Crossover Design

This design involves randomly assigning participants to one of two or more treatment groups, with each group receiving one treatment during the first phase of the study and then switching to a different treatment during the second phase.

Split-plot Design

In this design, the researcher manipulates one or more variables at different levels and uses a randomized block design to control for other variables.

Nested Design

This design involves grouping participants within larger units, such as schools or households, and then randomly assigning these units to different treatment groups.

Laboratory Experiment

Laboratory experiments are conducted under controlled conditions, which allows for greater precision and accuracy. However, because laboratory conditions are not always representative of real-world conditions, the results of these experiments may not be generalizable to the population at large.

Field Experiment

Field experiments are conducted in naturalistic settings and allow for more realistic observations. However, because field experiments are not as controlled as laboratory experiments, they may be subject to more sources of error.

Experimental Design Methods

Experimental design methods refer to the techniques and procedures used to design and conduct experiments in scientific research. Here are some common experimental design methods:

Randomization

This involves randomly assigning participants to different groups or treatments to ensure that any observed differences between groups are due to the treatment and not to other factors.

Control Group

The use of a control group is an important experimental design method that involves having a group of participants that do not receive the treatment or intervention being studied. The control group is used as a baseline to compare the effects of the treatment group.

Blinding involves keeping participants, researchers, or both unaware of which treatment group participants are in, in order to reduce the risk of bias in the results.

Counterbalancing

This involves systematically varying the order in which participants receive treatments or interventions in order to control for order effects.

Replication

Replication involves conducting the same experiment with different samples or under different conditions to increase the reliability and validity of the results.

This experimental design method involves manipulating multiple independent variables simultaneously to investigate their combined effects on the dependent variable.

This involves dividing participants into subgroups or blocks based on specific characteristics, such as age or gender, in order to reduce the risk of confounding variables.

Data Collection Method

Experimental design data collection methods are techniques and procedures used to collect data in experimental research. Here are some common experimental design data collection methods:

Direct Observation

This method involves observing and recording the behavior or phenomenon of interest in real time. It may involve the use of structured or unstructured observation, and may be conducted in a laboratory or naturalistic setting.

Self-report Measures

Self-report measures involve asking participants to report their thoughts, feelings, or behaviors using questionnaires, surveys, or interviews. These measures may be administered in person or online.

Behavioral Measures

Behavioral measures involve measuring participants’ behavior directly, such as through reaction time tasks or performance tests. These measures may be administered using specialized equipment or software.

Physiological Measures

Physiological measures involve measuring participants’ physiological responses, such as heart rate, blood pressure, or brain activity, using specialized equipment. These measures may be invasive or non-invasive, and may be administered in a laboratory or clinical setting.

Archival Data

Archival data involves using existing records or data, such as medical records, administrative records, or historical documents, as a source of information. These data may be collected from public or private sources.

Computerized Measures

Computerized measures involve using software or computer programs to collect data on participants’ behavior or responses. These measures may include reaction time tasks, cognitive tests, or other types of computer-based assessments.

Video Recording

Video recording involves recording participants’ behavior or interactions using cameras or other recording equipment. This method can be used to capture detailed information about participants’ behavior or to analyze social interactions.

Data Analysis Method

Experimental design data analysis methods refer to the statistical techniques and procedures used to analyze data collected in experimental research. Here are some common experimental design data analysis methods:

Descriptive Statistics

Descriptive statistics are used to summarize and describe the data collected in the study. This includes measures such as mean, median, mode, range, and standard deviation.

Inferential Statistics

Inferential statistics are used to make inferences or generalizations about a larger population based on the data collected in the study. This includes hypothesis testing and estimation.

Analysis of Variance (ANOVA)

ANOVA is a statistical technique used to compare means across two or more groups in order to determine whether there are significant differences between the groups. There are several types of ANOVA, including one-way ANOVA, two-way ANOVA, and repeated measures ANOVA.

Regression Analysis

Regression analysis is used to model the relationship between two or more variables in order to determine the strength and direction of the relationship. There are several types of regression analysis, including linear regression, logistic regression, and multiple regression.

Factor Analysis

Factor analysis is used to identify underlying factors or dimensions in a set of variables. This can be used to reduce the complexity of the data and identify patterns in the data.

Structural Equation Modeling (SEM)

SEM is a statistical technique used to model complex relationships between variables. It can be used to test complex theories and models of causality.

Cluster Analysis

Cluster analysis is used to group similar cases or observations together based on similarities or differences in their characteristics.

Time Series Analysis

Time series analysis is used to analyze data collected over time in order to identify trends, patterns, or changes in the data.

Multilevel Modeling

Multilevel modeling is used to analyze data that is nested within multiple levels, such as students nested within schools or employees nested within companies.

Applications of Experimental Design 

Experimental design is a versatile research methodology that can be applied in many fields. Here are some applications of experimental design:

• Medical Research: Experimental design is commonly used to test new treatments or medications for various medical conditions. This includes clinical trials to evaluate the safety and effectiveness of new drugs or medical devices.
• Agriculture : Experimental design is used to test new crop varieties, fertilizers, and other agricultural practices. This includes randomized field trials to evaluate the effects of different treatments on crop yield, quality, and pest resistance.
• Environmental science: Experimental design is used to study the effects of environmental factors, such as pollution or climate change, on ecosystems and wildlife. This includes controlled experiments to study the effects of pollutants on plant growth or animal behavior.
• Psychology : Experimental design is used to study human behavior and cognitive processes. This includes experiments to test the effects of different interventions, such as therapy or medication, on mental health outcomes.
• Engineering : Experimental design is used to test new materials, designs, and manufacturing processes in engineering applications. This includes laboratory experiments to test the strength and durability of new materials, or field experiments to test the performance of new technologies.
• Education : Experimental design is used to evaluate the effectiveness of teaching methods, educational interventions, and programs. This includes randomized controlled trials to compare different teaching methods or evaluate the impact of educational programs on student outcomes.
• Marketing : Experimental design is used to test the effectiveness of marketing campaigns, pricing strategies, and product designs. This includes experiments to test the impact of different marketing messages or pricing schemes on consumer behavior.

Examples of Experimental Design 

Here are some examples of experimental design in different fields:

• Example in Medical research : A study that investigates the effectiveness of a new drug treatment for a particular condition. Patients are randomly assigned to either a treatment group or a control group, with the treatment group receiving the new drug and the control group receiving a placebo. The outcomes, such as improvement in symptoms or side effects, are measured and compared between the two groups.
• Example in Education research: A study that examines the impact of a new teaching method on student learning outcomes. Students are randomly assigned to either a group that receives the new teaching method or a group that receives the traditional teaching method. Student achievement is measured before and after the intervention, and the results are compared between the two groups.
• Example in Environmental science: A study that tests the effectiveness of a new method for reducing pollution in a river. Two sections of the river are selected, with one section treated with the new method and the other section left untreated. The water quality is measured before and after the intervention, and the results are compared between the two sections.
• Example in Marketing research: A study that investigates the impact of a new advertising campaign on consumer behavior. Participants are randomly assigned to either a group that is exposed to the new campaign or a group that is not. Their behavior, such as purchasing or product awareness, is measured and compared between the two groups.
• Example in Social psychology: A study that examines the effect of a new social intervention on reducing prejudice towards a marginalized group. Participants are randomly assigned to either a group that receives the intervention or a control group that does not. Their attitudes and behavior towards the marginalized group are measured before and after the intervention, and the results are compared between the two groups.

When to use Experimental Research Design 

Experimental research design should be used when a researcher wants to establish a cause-and-effect relationship between variables. It is particularly useful when studying the impact of an intervention or treatment on a particular outcome.

Here are some situations where experimental research design may be appropriate:

• When studying the effects of a new drug or medical treatment: Experimental research design is commonly used in medical research to test the effectiveness and safety of new drugs or medical treatments. By randomly assigning patients to treatment and control groups, researchers can determine whether the treatment is effective in improving health outcomes.
• When evaluating the effectiveness of an educational intervention: An experimental research design can be used to evaluate the impact of a new teaching method or educational program on student learning outcomes. By randomly assigning students to treatment and control groups, researchers can determine whether the intervention is effective in improving academic performance.
• When testing the effectiveness of a marketing campaign: An experimental research design can be used to test the effectiveness of different marketing messages or strategies. By randomly assigning participants to treatment and control groups, researchers can determine whether the marketing campaign is effective in changing consumer behavior.
• When studying the effects of an environmental intervention: Experimental research design can be used to study the impact of environmental interventions, such as pollution reduction programs or conservation efforts. By randomly assigning locations or areas to treatment and control groups, researchers can determine whether the intervention is effective in improving environmental outcomes.
• When testing the effects of a new technology: An experimental research design can be used to test the effectiveness and safety of new technologies or engineering designs. By randomly assigning participants or locations to treatment and control groups, researchers can determine whether the new technology is effective in achieving its intended purpose.

How to Conduct Experimental Research

Here are the steps to conduct Experimental Research:

• Identify a Research Question : Start by identifying a research question that you want to answer through the experiment. The question should be clear, specific, and testable.
• Develop a Hypothesis: Based on your research question, develop a hypothesis that predicts the relationship between the independent and dependent variables. The hypothesis should be clear and testable.
• Design the Experiment : Determine the type of experimental design you will use, such as a between-subjects design or a within-subjects design. Also, decide on the experimental conditions, such as the number of independent variables, the levels of the independent variable, and the dependent variable to be measured.
• Select Participants: Select the participants who will take part in the experiment. They should be representative of the population you are interested in studying.
• Randomly Assign Participants to Groups: If you are using a between-subjects design, randomly assign participants to groups to control for individual differences.
• Conduct the Experiment : Conduct the experiment by manipulating the independent variable(s) and measuring the dependent variable(s) across the different conditions.
• Analyze the Data: Analyze the data using appropriate statistical methods to determine if there is a significant effect of the independent variable(s) on the dependent variable(s).
• Draw Conclusions: Based on the data analysis, draw conclusions about the relationship between the independent and dependent variables. If the results support the hypothesis, then it is accepted. If the results do not support the hypothesis, then it is rejected.
• Communicate the Results: Finally, communicate the results of the experiment through a research report or presentation. Include the purpose of the study, the methods used, the results obtained, and the conclusions drawn.

Purpose of Experimental Design 

The purpose of experimental design is to control and manipulate one or more independent variables to determine their effect on a dependent variable. Experimental design allows researchers to systematically investigate causal relationships between variables, and to establish cause-and-effect relationships between the independent and dependent variables. Through experimental design, researchers can test hypotheses and make inferences about the population from which the sample was drawn.

Experimental design provides a structured approach to designing and conducting experiments, ensuring that the results are reliable and valid. By carefully controlling for extraneous variables that may affect the outcome of the study, experimental design allows researchers to isolate the effect of the independent variable(s) on the dependent variable(s), and to minimize the influence of other factors that may confound the results.

Experimental design also allows researchers to generalize their findings to the larger population from which the sample was drawn. By randomly selecting participants and using statistical techniques to analyze the data, researchers can make inferences about the larger population with a high degree of confidence.

Overall, the purpose of experimental design is to provide a rigorous, systematic, and scientific method for testing hypotheses and establishing cause-and-effect relationships between variables. Experimental design is a powerful tool for advancing scientific knowledge and informing evidence-based practice in various fields, including psychology, biology, medicine, engineering, and social sciences.

Advantages of Experimental Design 

Experimental design offers several advantages in research. Here are some of the main advantages:

• Control over extraneous variables: Experimental design allows researchers to control for extraneous variables that may affect the outcome of the study. By manipulating the independent variable and holding all other variables constant, researchers can isolate the effect of the independent variable on the dependent variable.
• Establishing causality: Experimental design allows researchers to establish causality by manipulating the independent variable and observing its effect on the dependent variable. This allows researchers to determine whether changes in the independent variable cause changes in the dependent variable.
• Replication : Experimental design allows researchers to replicate their experiments to ensure that the findings are consistent and reliable. Replication is important for establishing the validity and generalizability of the findings.
• Random assignment: Experimental design often involves randomly assigning participants to conditions. This helps to ensure that individual differences between participants are evenly distributed across conditions, which increases the internal validity of the study.
• Precision : Experimental design allows researchers to measure variables with precision, which can increase the accuracy and reliability of the data.
• Generalizability : If the study is well-designed, experimental design can increase the generalizability of the findings. By controlling for extraneous variables and using random assignment, researchers can increase the likelihood that the findings will apply to other populations and contexts.

Limitations of Experimental Design

Experimental design has some limitations that researchers should be aware of. Here are some of the main limitations:

• Artificiality : Experimental design often involves creating artificial situations that may not reflect real-world situations. This can limit the external validity of the findings, or the extent to which the findings can be generalized to real-world settings.
• Ethical concerns: Some experimental designs may raise ethical concerns, particularly if they involve manipulating variables that could cause harm to participants or if they involve deception.
• Participant bias : Participants in experimental studies may modify their behavior in response to the experiment, which can lead to participant bias.
• Limited generalizability: The conditions of the experiment may not reflect the complexities of real-world situations. As a result, the findings may not be applicable to all populations and contexts.
• Cost and time : Experimental design can be expensive and time-consuming, particularly if the experiment requires specialized equipment or if the sample size is large.
• Researcher bias : Researchers may unintentionally bias the results of the experiment if they have expectations or preferences for certain outcomes.
• Lack of feasibility : Experimental design may not be feasible in some cases, particularly if the research question involves variables that cannot be manipulated or controlled.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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• Science Snippets Blog

What is DOE? Design of Experiments Basics for Beginners

[This blog was a favorite last year, so we thought you'd like to see it again. Send us your comments!]. Whether you work in engineering, R&D, or a science lab, understanding the basics of experimental design can help you achieve more statistically optimal results from your experiments or improve your output quality.

This article is posted on our Science Snippets Blog .

Using  Design of Experiments (DOE)  techniques, you can determine the individual and interactive effects of various factors that can influence the output results of your measurements. You can also use DOE to gain knowledge and estimate the best operating conditions of a system, process or product.

DOE applies to many different investigation objectives, but can be especially important early on in a screening investigation to help you determine what the most important factors are. Then, it may help you optimize and better understand how the most important factors that you can regulate influence the responses or critical quality attributes.

Another important application area for DOE is in making production more effective by identifying factors that can reduce material and energy consumption or minimize costs and waiting time. It is also valuable for robustness testing to ensure quality before releasing a product or system to the market.

What’s the Alternative?

In order to understand why Design of Experiments is so valuable, it may be helpful to take a look at what DOE helps you achieve. A good way to illustrate this is by looking at an alternative approach, one that we call the  “COST”  approach. The COST ( C hange  O ne  S eparate factor at a  T ime) approach might be considered an intuitive or even logical way to approach your experimentation options (until, that is, you have been exposed to the ideas and thinking of DOE).

Let’s consider the example of a small chemical reaction where the goal is to find optimal conditions for yield. In this example, we can vary only two elements, or factors:

• the volume of the reaction container (between 500 and 700 ml), and
• the pH of the solution (between 2.5 and 5).

We change the experimental factors and measure the response outcome, which in this case, is the yield of the desired product. Using the COST approach, we can vary just one of the factors at time to see what affect it has on the yield.

So, for example, first we might fix the pH at 3, and change the volume of the reaction container from a low setting of 500ml to a high of 700ml. From that we can measure the yield.

Below is an example of a table that shows the yield that was obtained when changing the volume from 500 to 700 ml. In the scatterplot on the right, we have plotted the measured yield against the change in reaction volume, and it doesn’t take long to see that the best volume is located at 550 ml.

Next, we evaluate what will happen when we fix the volume at 550 ml (the optimal level) and start to change the second factor. In this second experimental series, the pH is changed from 2.5 to 5.0 and you can see the measured yields. These are listed in the table and plotted below. From this we can see that the optimal pH is around 4.5.

The optimal combination for the best yield would be a volume of 550 ml and pH 4.5. Sounds good right? But, let’s consider this a bit more.

Gaining a Better Perspective With DOE

What happens when we take more of a bird’s eye perspective, and look at the overall experimental map by number and order of experiments?

For example, in the first experimental series (indicated on the horizontal axis below), we moved the experimental settings from left to right, and we found out that 550 was the optimal volume.

Then in the second experimental series, we moved from bottom to top (as shown in the scatterplot below) and after a while we found out that the best yield was at experiment number 10 (4.5 pH).

The problem here is that we are not really certain whether the experimental point number 10 is truly the best one. The risk is that we have perceived that as being the optimum without it really being the case. Another thing we may question is the number of experiments we used. Have we used the optimal number of runs for experiments?

Zooming out and picturing what we have done on a map, we can see that we have only been exploiting a very small part of the entire experimental space. The true relationship between pH and volume is represented by the Contour Plot pictured below. We can see that the optimal value would be somewhere at the top in the larger red area.

So the problem with the COST approach is that we can get very different implications if we choose other starting points. We perceive that the optimum was found, but the other— and perhaps more problematic thing—is that we didn’t realize that continuing to do additional experiments would produce even higher yields.

How to Design Better Experiments

Instead, using the DOE approach, we can build a map in a much better way. First, consider the use of just two factors, which would mean that we have a limited range of experiments.  As the contour plot below shows, we would have at least four experiments (defining the corners of a rectangle.)

These four points can be optimally supplemented by a couple of points representing the variation in the interior part of the experimental design.

The important thing here is that when we start to evaluate the result, we will obtain very valuable information about the direction in which to move for improving the result. We will understand that we should reposition the experimental plan according to the dashed arrow.

However, DOE is NOT limited to looking at just two factors. It can be applied to three, four or many more factors.

If we take the approach of using three factors, the experimental protocol will start to define a cube rather than a rectangle. So the factorial points will be the corners of the cube.

In this way, DOE allows you to construct a carefully prepared set of representative experiments, in which all relevant factors are varied simultaneously.

DOE is about creating an entity of experiments that work together to map an interesting experimental region. So with DOE we can prepare a set of experiments that are optimally placed to bring back as much information as possible about how the factors are influencing the responses.

Plus, we will we have support for different types of regression models. For example, we can estimate what we call a linear model, or an interaction model, or a quadratic model. So the selected experimental plan will support a specific type of model.

Why Is DOE a Better Approach?

We can see three main reasons that DOE Is a better approach to experiment design than the COST approach.

DOE suggests the correct number of runs needed (often fewer than used by the COST approach)

DOE provides a model for the direction to follow

Many factors can be used (not just two)

In summary, the benefits of DOE are:

• An organized approach that connects experiments in a rational manner
• The influence of and interactions between all factors can be estimated
• More precise information is acquired in fewer experiments
• Results are evaluated in the light of variability
• Support for decision-marketing: map of the system (response contour plot)

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• Statistics for Experimenters
• Hunter Award

101 Ways to Design an Experiment, or Some Ideas About Teaching Design of Experiments by William G. Hunter

williamghunter.net > Articles > 101 Ways to Design an Experiment

I want to share some ideas about teaching design of experiments. They are related to something I have often wondered about: whether it is possible to let students experience first-hand all the steps involved in an experimental investigation-thinking of the problem, deciding what experiments might shed light on the problem, planning the runs to be made, carrying them out, analyzing the results, and writing a report summarizing the work. One curiosity about most courses on experimental designing, it seems to me, is that students get no practice designing realistic experiments although, from homework assignments, they do get practice analyzing data. Clearly, however, because of limitations of time and money, if students are to design experiments and actually carry them out, they cannot be involved with elaborate investigations. Therefore, the key question is this: Is it feasible for students to devise their own simple experiments and carry them through to completion and, if so, is it of any educational value to have them do so? I believe the answer to both parts of the question is yes, and the purpose of this paper is to explain why.

The particular design course I have taught most often is a one-semester course that includes these standard statistical techniques: t-tests (paired and unpaired), analysis of variance (primarily for one-way and two-way layouts), factorial and fractional factorial designs (emphasis given to two-level designs), the method of least squares (for linear and nonlinear models), and response surface methodology. The value of randomization and blocking is stressed. Special attention is given to these questions: What are the assumptions being made? What if they are violated? What common pitfalls are encountered in practice? What precautions can be taken to avoid these pitfalls? In analyzing data how can one determine whether the model is adequate? Homework problems provide ample opportunity for carefully examining residuals, especially by plotting them. The material for this course is discussed in the context of the iterative nature of experimental investigations.

Most of those who have taken this course have been graduate students, principally in engineering (chemical, civil, mechanical, industrial, agricultural) but also in a variety of other fields including statistics, food science, forestry, chemistry, and biology. There is a prerequisite of a one-semester introductory statistics course, but this requirement is customarily waived for graduate students with the understanding that they do a little extra work to catch up.

Simulated Data

One possibility is to use simulated data, and the scope here is wide, especially with the availability of computers. At times I have given assignments of this kind, especially response surface problems. Each student receives his or her own sets of data based upon the designs he or she chooses.

The problem might be set up as one involving a chemist who wishes to find the best settings of these five variables-temperature, concentration, pH, stirring rate, and amount of catalyst-and to determine the local geography of the response surface(s) near the optimum. To define the region of operability, ranges are specified for each of these variables. Perhaps more than one response can be measured, for instance, yield and cost. The student is given a certain budget, either in terms of runs or money, the latter being appropriate if there is an option provided for different types of experiments which have different costs. The student can ask for data in, say, three stages. Between these stages the accumulated data can be analyzed so that future experiments can be planned on the basis of all available information.

In generating the data, which contains experimental error, there are many possibilities. Different models can be used for each student, the models not necessarily being the usual simple first-order or second-order linear models. Not all variables need to be important, that is, some may be dummy variables (different ones for different students). Time trends and other abnormalities can be deliberately introduced into the data provided to the students.

The student prepares a report including a summary of the most important facts discovered about his or her system and perhaps containing a contour map of the response surface(s) for the two most important variables (if three of the five variables are dummies, this map should correspond to the true surface from which the data were generated). It is instructive then to compare each student's findings with the corresponding true situation.

Students enjoy games of this type and learn a considerable amount from them. For many it is the first time they realize just how frustrating the presence of an appreciable amount of experimental error can be. The typical prearranged undergraduate laboratory experiments in physics and chemistry, of course, have all important known sources of experimental error removed (typically the data are supposed to fall on a straight line-exactly-or else).

One's first reaction might be that there are not enough possibilities for experiments of this kind. But this is incorrect, as is illustrated by Table 1, which lists some of the experiments reported by the students. Experiments number 1-63 are of the home type and experiments number 64-101 are of the laboratory type. Note the variety of studies done. To save space, for most variables the levels used are not given. Anyway, they are not essential for our purposes here. Most of these experiments were factorial designs. Let us look briefly at the first two home experiments and the first two laboratory experiments.

Bicycle Experiment

In experiment number 1 the student, Norman Miller, using a factorial design with all points replicated, studied the effects of three variables-seat height (26, 30 inches), light generator (on or off), and tire pressure (40, 55 psi)-on two responses-time required to ride his bicycle over a particular course and his pulse rate at the finish of each run (pulse rate at the start was virtually constant). To him the most surprising result was how much he was slowed down by having the generator on. The average time for each run was approximately 50 seconds. He discovered that raising the seat reduced the time by about 10 seconds, having the generator on increased it by about one-third that amount and inflating the tires to 55 psi reduced the time by about the same amount that the generator increased it. He planned further experiments.

Popcorn Experiment

In experiment number 2 the student, Karen Vlasek, using a factorial design with four replicated center points, determined the effects of three variables on the amount of popcorn produced. She found, for example, that although double the yield was obtained with the gourmet popcorn, it cost three times as much as the regular popcorn. By using this experimental design she discovered approximately what combination of variables gave her best results. She noted that it differed from those recommended by the manufacturer of her popcorn popper and both suppliers of popcorn.

Dilution experiment

In experiment number 64 the student, Dean Hafeman, studied a routine laboratory procedure (a dilution) that was performed many times each day where he worked-almost on a mass production basis. The manufacturer of the equipment used for this work emphasized that the key operations, the raising and lowering of two plungers, had to be done slowly for good results. The student wondered what difference it would make if these operations were done quickly. He set up a factorial design in which the variables were the raising and lowering of plunger A and the raising and lowering of plunger B. The two levels of each variable were slow and fast. To his surprise, he found that none of the variables had any measurable effect on the readings. This conclusion had important practical implications in his laboratory because it meant that good results could be obtained even if the plungers were moved quickly; consequently a considerable amount of time could be saved in doing this routing work.

Trouble-shooting Experiment

In experiment number 65 the student, Rodger Melton, solved a trouble-shooting problem that he encountered in his research work. In one piece of his apparatus an extremely small quantity of a certain chemical was distilled to be collected in a second piece of the apparatus. Unfortunately, some of this material condensed prematurely in the line between these two pieces of apparatus. Was there a way to prevent this? By using a factorial design the problem was solved, it being discovered that by suitably adjusting the voltage and using a J-tube none of the material condensed prematurely. The column temperature, which was discovered to be minor consequence as far as premature condensation was concerned (a surprise), could be set to maximize throughput.

Most Popular Experiments

The most popular home experiments have concerned cooking since recipes lend themselves so readily to variations. What to measure for the response has sometimes created a problem. Usually a quality characteristic such as taste has been determined (preferably independently by a number of judges) on a 1-5 or 1-10 scale. Growing seeds has also been an easy and popular experiment. In the laboratory experiments, sensitivity or robustness tests have been the most common (the dilution experiment, number 65, discussed above is of this type). Typically the experimenter varies the conditions for a standard analytical procedure (for example, for the measurement of chemical oxygen demand, COD) to see how much the measured value is affected. That is, if the standard procedure calls for the addition of 20 ml. of a particular chemical, 18 ml. and 22 ml. might be tried. Results from such tests are revealing no matter which way they turn out. One student, for example, concluded ``The results sort of speak for themselves. The test is not very robust.'' Another student, who studied a different test, reported ``The results of the Yates analysis show that the COD test is indeed robust.''

Structuring the Assignment

I have always made these assignments completely open, saying that they could study anything that interested them. I have tended to favor home rather than laboratory experiments. I have suggested they choose something they care about, preferably something they've wondered about. Such projects seem to turn out better than those picked for no particularly good reason. Here is how a few of the reports began: ``Ever since we came to Madison my family has experienced difficulty in making bread that will rise properly.'' ``Since moving to Madison, my green thumb has turned black. Every plant I have tried to grow has died.'' (Nothing works in Madison?) ``This experiment deals with how best to prepare pancakes to satisfy the group of four of us living together.'' ``I rent an efficiency on the second floor of an apartment building which has cooking facilities on the first floor only. When I cook rice, my staple food,I have to make one to three visits to the kitchen to make sure it is ready to be served and not burned. Because of this inconvenience, I wanted to study the effects of certain variables on the cooking time of rice.'' ``My wife and I were wondering if our oldest daughter had a favorite toy.'' ``For the home brewer, a small kitchen blender does a good job of grinding malt, provided the right levels of speed, batch size and time are used. This is the basis of the experimental design.'' ``During my career as a beer drinker, various questions have arisen.'' ``I do much of the maintenance and repair work around my home, and some of the repairs require the use of epoxy glue. I was curious about some of the factors affecting its performance.'' ``My wife and I are interested in indoor plants, and often we like to give them as gifts. We usually select a cutting from one of our fifty or so plants, put it in a glass of water until it develops roots, and then pot it. We have observed that sometimes the cutting roots quickly and sometimes it roots slowly, so we decided to experiment with several factors that we thought might be important in this process.'' ``I chose to find out how my shotguns were firing. I reload my own shells with powders that were recommended to me, one for short range shooting and one for long range shooting. I had my doubts if the recommendations were valid.''

What Did the Students Learn?

The conclusion reached in this last experiment was: ``As it looks now, I should use my Gun A with powder C for close range shooting, such as for grouse and woodcock. I should use gun B and powder D for longer range shooting as for ducks and geese.'' As is illustrated by this example and the first four discussed above, the students sometimes learned things that were directly useful to them. Some other examples: ``Spending $70 extra to buy tape deck 2 is not justified as the difference in sound is better with the other, or probably there is no difference. The synthesizer appears not to affect the quality of the sound.'' In operating my calculator I can anticipate increasing operation time by an additional 15 minutes and 23 seconds on the average by charging 60 minutes instead of 30 minutes.'' ``In conclusion, the Chinese dumplings turned out very pretty and very delicious, especially the ones with thin skins. I think this was a successful experiment.

Naturally, not all experiments were successful. ``A better way to have run the experiment would have been to...'' Various troubles arose. ``The reason that there is only one observation for the eighth row is that one of the cups was knocked over by a curious cat.'' ``One observation made during the experiment was that the child's posture may have affected the duration of the ride. Mark (13 pounds) leaned back, thus distributing his weight more evenly. On the other hand, Mike (22 pounds) preferred to sit forward, which may have made the restoring action of the spring more difficult.'' (The trouble here was that the variable the student wanted to study was weight, not posture.) Another student, who was studying factors that affected how fast snow melted on sidewalks, had some of his data destroyed because the sun came out brightly (and unexpectedly) one day near the end of his experiment and melted all the snow.

Because of such troubles these simple experiments have served as useful vehicles for discussing important practical points that arise in more serious scientific investigations. Excellent questions for this purpose have arisen from these studies. ``Do I really need to use a completely randomized experiment? It will take much longer to do that way?'' There have been good examples that illustrate the sequential nature of experimentation and show how carefully conceived experimental designs can help in solving problems.''...This must have been the main reason why the first experiment completely failed. I decided to try another factorial design. Synchronization of the flash unit and camera still bothered me. I decided to experiment with...'' some other factors.

As a result of these projects students seem to get a much better appreciation of the efficiency and beauty of experimental designs. For example, in this last experiment the student concluded: ``The factorial design proved to be efficient in solving the problem. I did get off on the wrong track initially, but the information learned concerning synchronization is quite valuable.'' Another student: ``It is interesting to see how a few experiments can give so much information.''

There is another point, and it is not the least important. Most of the students had fun with these projects. And I did, too. Just looking through Table 1 suggests why this is so, I think. One report ended simply: ``This experiment was really fun!'' Many students have reported that this was the best part of the course.

There is a tendency sometimes for experimenters to discount what they have learned, this being true not only for students in this class, but also for experimenters in general. That is, they learn more than they realize. Hindsight is the culprit. On pondering a certain conclusion, one is prone to say ``Oh yes, that makes sense. Yes, that's the way it should be. That's what I would have expected.'' While this reaction is often correct, one is sometimes just fooling oneself, that is, interrogation at the outset would have produced exactly the opposite opinion. So that students could more accurately gauge what they learned from their simple experiments, I tried the following and it seemed to work: after having decided on the experimental runs to perform, the student guessed what his or her major conclusions would be and wrote them down. Upon completion of the assignment, these guesses were checked against the actual results, which immediately provided a clear picture of what was learned (the surprises) and what was confirmed (the non-surprises).

I now tend to spend much more time introducing each new topic than I used to. Providing appropriate motivation is extremely important. For classes I have had the privilege of teaching-whether in universities or elsewhere-I have found that it has been better to use concrete examples followed by the general theory rather than the reverse. I now try to describe a particular problem in some detail, preferably a real one with which I am familiar, and then pose the question: What would YOU do? I find it helpful to resist the temptation to move on too quickly to the prepared lecture so that there is ample time for students to consider this question seriously, to discuss it, to ask questions of clarification, to express ideas they have, and ultimately (and this really the object of the exercise) to realize that a genuine problem exists and they do not know how to solve it. They are then eager to learn. And after we have finished with that particular topic they know they have learned something of value. (I realize as I write this that I have been strongly influenced by George Barnard, who masterfully conducted a seminar in this manner at Imperial College, London, in 1964-65, which I was fortunate to have attended.)

Current examples are well-received, especially controversies (for example, weather modification experiments). Some useful sources are court cases, advertisements, TV and radio commercials, and ``Consumer Reports''. An older controversy still of considerable interest from a pedagogical point of view is the AD-X2 battery additive case. Gosset's comments on the Lanarkshire Milk Experiment are still illuminating. Sometimes trying to get the data that support a particular TV commercial or the facts from both parties of a dispute has made an interesting side project to carry along through a semester.

Having each student exercise his or her own initiative in thinking up an experiment and carrying it through to completion has turned out successfully. Using games involving simulated data has also been useful. I have incorporated such projects, principally of the former type, into courses I have taught, and I urge others to consider doing the same. Why?

First of all, it's fun. The students have generally welcomed the opportunity to learn something about a particular question they have wondered about. I have been fascinated to see what they have chosen to study and what conclusions they have reached, so it has been fun for me, too. The students and I have certainly learned interesting things we did not know before. Why doesn't my bread rise? Why don't my flowers grow? Is this analytical procedure robust? Will carrying a crutch make it easier for me to get a ride hitchhiking? (Incidentally, it made it harder.)

Secondly, the students have gotten a lot out of such experiences. There is a definite deepening of understanding that comes from having been through a study from start to finish-deciding on a problem, the variables, the ranges of the variables, and how to measure the response(s), actually running the experiment and collecting the data, analyzing the results, learning what the practical consequences are, and finally writing a report. Being veterans, not of the war certainly but of a minor skirmish at least, the students seem more comfortable and confident with the entire subject of the design of experiments, especially as they share their experiences with one another.

Thirdly, I have found it particularly worthwhile to discuss with them in class some of the practical questions that naturally emerge from these studies. ``What can I do about missing data?'' ``These first three readings are questionable because I think I didn't have my technique perfected then-What should I do?'' ``A most unusual thing happened during this run, so should I analyze this result with all the others or leave it out?'' They are genuinely interested in such questions because they have actually encountered them, not just read about them in a textbook. Sometimes there is no simple answer, and lively and valuable discussions then occur. Such discussions, I hope, help them understand that, when they confront real problems later on which refuse to look like those in the textbooks no matter how they are viewed, there are alternatives to pretending they do and charging ahead regardless or forgetting about them in hopes they will go away or adopting a ``non-statistical'' approach-in a word, there are alternatives to panic.

Table 1. List of some studies done by students in an experimental design course.

• variables: seat height (26, 30 inches), generator (off,on), tire pressure (40, 55 psi) responses: time to complete fixed course on bicycle and pulse rate at finish
• variables: brand of popcorn (ordinary, gourmet), size of batch (1/3,2/3 cup), popcorn to oil ratio (low, high) responses: yield of popcorn
• variables: amount of yeast, amount of sugar, liquid (milk, water), rise temperature, rise time responses: quality of bread, especially the total rise
• variables: number of pills, amount of cough syrup, use of vaporizer responses: how well twins, who had colds, slept during the night
• variables: speed of film, light (normal, diffused), shutter speed responses: quality of slides made close up with flash attachment on camera
• variables: hours of illumination, water temperature, specific gravity of water responses: growth rate of algae in salt water aquarium
• variables: temperature, amount of sugar, food prior to drink (water, salted popcorn) responses: taste of Koolaid
• variables: direction in which radio is facing, antenna angle, antenna slant responses: strength of radio signal from particular AM station in Chicago
• variables: blending speed, amount of water, temperature of water, soaking time before blending responses: blending time for soy beans
• variables: charge time, digits fixed, number of calculations performed responses: operation time for pocket calculator
• variables: clothes dryer (A,B), temperature setting, load responses: time until dryer stops
• variables: pan (aluminum, iron), burner on stove, cover for pan (no, yes) responses: time to boil water
• variables: aspirin buffered? (no, yes) dose, water temperature responses: hours of relief from migraine headache
• variables: amount of milk powder added to milk, heating temperature, incubation temperature responses: taste comparison of homemade yogurt and commercial brand
• variables: pack on back (no, yes), footwear (tennis shoes, boots), run (7, 14 flights of steps) responses: time required to run up steps and heartbeat at top
• variables: width to height ratio of sheet of balsa wood, slant angle, dihedral angle, weight added, thickness of wood responses: length of flight of model airplane
• variables: level of coffee in cup, devices (nothing, spoon placed across top of cup facing up), speed of walking responses: how much coffee spilled while walking
• variables: type of stitch, yarn gauge, needle size responses: cost of knitting scarf, dollars per square foot
• variables: type of drink (beer, rum), number of drinks, rate of drinking, hours after last meal responses: time to get steel ball through a maze
• variables: size of order, time of day, sex of server responses: cost of order of french fries, in cents per ounce
• variables: brand of gasoline, driving speed, temperature responses: gas mileage for car
• variables: stamp (first class, air mail), zip code (used, not used), time of day when letter mailed responses: number of days required for letter to be delivered to another city
• variables: side of face (left, right), beard history (shaved once in two years0-sideburns, shaved over 600 times in two years-just below sideburns) responses: length of whiskers 3 days after shaving
• variables: eyes used (both, right), location of observer, distance responses: number of times (out of 15) that correct gender of passerby was determined by experimenter with poor eyesight wearing no glasses
• variables: distance to target, guns (A,B), powders(C,D) responses: number of shot that penetrated a one foot diameter circle on the target
• variables: oven temperature, length of heating, amount of water responses: height of cake
• variables: strength of developer, temperature, degree of agitation responses: density of photographic film
• variables: brand of rubber band, size, temperature responses: length of rubber band before it broke
• variables: viscosity of oil, type of pick-up shoes, number of teeth in gear responses: speed of H.O. scale slot racers
• variables: type of tire, brand of gas, driver (A,B) responses: time for car to cover one-quarter mile
• variables: temperature, stirring rate, amount of solvent responses: time to dissolve table salt
• variables: amounts of cooking wine, oyster sauce,sesame oil responses: taste of stewed chicken
• variables: type of surface, object (slide rule, ruler, silver dollar), pushed? (no,yes) responses: angle necessary to make object slide
• variables: ambient temperature, choke setting, number of charges responses: number of kicks necessary to start motorcycle
• variables: temperature, location in oven, biscuits covered while baking? (no,yes) responses: time to bake biscuits
• variables: temperature of water, amount of grease, amount of water conditioner responses: quantity of suds produced in kitchen blender
• variables: person putting daughter to bed (mother, father), bed time, place (home, grandparents) responses: toys child chose to sleep with
• variables: amount of light in room, type of music played, volume responses: correct answers on simple arithmetic test, time required to complete test, words remembered (from list of 15)
• variables: amounts of added Turkish, Latakia, and Perique tobaccos responses: bite, smoking characteristics, aroma, and taste of tobacco mixture
• variables: temperature, humidity, rock salt responses: time to melt ice
• variables: number of cards dealt at one time, position of picker relative to the dealer responses: points in games of sheepshead, a card game
• variables: marijuana (no, yes),tequila (no, yes),sauna (no, yes) responses: pleasure experienced in subsequent sexual intercourse
• variables: amounts of flour, eggs, milk responses: taste of pancakes, consensus of group of four living together
• variables: brand of suntan lotion, altitude, skier responses: time to get sun burned
• variables: amount of sleep the night before, substantial exercise during the day? (no, yes), eat right before going to bed? (no, yes) responses: soundness of sleep, average reading from five persons
• variables: brand of tape deck used for playing music, bass level, treble level, synthesizer? (no, yes) responses: clearness and quality of sound, and absence of noise
• variables: type of filter paper, beverage to be filtered, volume of beverage responses: time to filter
• variables: type of ski, temperature, type of wax responses: time to go down ski slope
• variables: ambient temperature for dough when rising, amount of vegetable oil, number of onions responses: four quality characteristics of pizza
• variables: amount of fertilizer, location of seeds (3 x 3 Latin square) responses: time for seeds to germinate
• variables: speed of kitchen blender, batch size of malt, blending time responses: quality of ground malt for brewing beer
• variables: soft drink (A,B), container (can, bottle), sugar free? (no, yes) responses: taste of drink from paper cup
• variables: child's weight (13, 22 pounds),spring tension (4, 8 cranks), swing orientation (level, tilted) responses: number of swings and duration of these swings obtained from an automatic infant swing
• variables: orientation of football, kick (ordinary, soccer style),steps taken before kick, shoe (soft, hard) responses: distance football was kicked
• variables: weight of bowling ball, spin, bowling lane (A, B) responses: bowling pins knocked down
• variables: distance from basket type of shot, location on floor responses: number of shots made (out of 10) with basketball
• variables: temperature, position of glass when pouring soft drink, amount of sugar added responses: amount of foam produced when pouring soft drink into glass
• variables: brand of epoxy glue, ratio of hardener to resin, thickness of application, smoothness of surface, curing time responses: strength of bond between two strips of aluminum
• variables: amount of plant hormone, water (direct from tap, stood out for 24 hours), window in which plant was put responses: root lengths of cuttings from purple passion vine after 21 days
• variables: amount of detergent (1/4, 1/2 cup), bleach (none, 1 cup), fabric softener (not used, used) responses: ability to remove oil and grape juice stains
• variables: skin thickness, water temperature, amount of salt responses: time to cook Chinese meat dumpling
• variables: appearance (with and without a crutch), location, time responses: time to get a ride hitchhiking and number of cars that passed before getting a ride
• variables: frequency of watering plants, use of plant food (no, yes), temperature of water responses: growth rate of house plants
• variables: plunger A up (slow, fast),plunger A down (slow, fast), plunger B up (slow, fast) plunger B down (slow, fast) responses: reproducibility of automatic diluter, optical density readings made with spectrophotometer
• variables: temperature of gas chromatograph column, tube type (U, J), voltage responses: size of unwanted droplet
• variables: temperature, gas pressure, welding speed responses: strength of polypropylene weld,manual operation
• variables: concentration of lysozyme, pH, ionic strength, temperature responses: rate of chemical reaction
• variables: anhydrous barium peroxide powder,sulfur,charcoal dust responses: length of time fuse powder burned and the evenness of burning
• variables: air velocity, air temperature, rice bed depth responses: time to dry wild rice
• variables: concentration of lactose crystal, crystal size, rate of agitation responses: spread ability of caramel candy
• variables: positions of coating chamber, distribution plate, and lower chamber responses: number of particles caught in a fluidized bed collector
• variables: proportional band, manual reset, regulator pressure responses: sensitivity of a pneumatic valve control system for a heat exchanger
• variables: chloride concentration, phase ratio, total amine concentration, amount of preservative added responses: degree of separation of zinc from copper accomplished by extraction
• variables: temperature, nitrate concentration, amount of preservative added responses: measured nitrate concentration in sewage, comparison of three different methods
• variables: solar radiation collector size, ratio of storage capacity to collector size, extent of short-term intermittency of radiation, average daily radiation on three successive days responses: efficiency of solar space-heating system, a computer simulation
• variables: pH, dissolved oxygen content of water, temperature responses: extent of corrosion of iron
• variables: amount of sulfuric acid, time of shaking milk-acid mixture, time of final tempering responses: measurement of butterfat content of milk
• variables: mode (batch, time-sharing), job size, system utilization (low, high) responses: time to complete job on computer
• variables: flow rate of carrier gas, polarity of stationary liquid phase, temperature responses: two different measures of efficiency of operation of gas chromatograph
• variables: pH of assay buffer, incubation time, concentration of binder responses: measured cortisol level in human blood plasma
• variables: aluminum, boron, cooling time responses: extent of rock candy fracture of cast steel
• variables: magnification, read out system (micrometer, electronic), stage light responses: measurement of angle with photogrammetric instrument
• variables: riser height, mold hardness, carbon equivalent responses: changes in height, width, and length dimensions of cast metal
• variables: amperage, contact tube height, travel speed, edge preparation responses: quality of weld made by submerged arc welding process
• variables: time, amount of magnesium oxide, amount of alloy responses: recovery of material by steam distillation
• variables: pH, depth, time responses: final moisture content of alfalfa protein
• variables: deodorant, concentration of chemical, incubation time responses: odor produced by material isolated from decaying manure, after treatment
• variables: temperature variation, concentration of cupric sulfate concentration of sulfuric acid responses: limiting currents on totaling disk electrode
• variables: air flow, diameter of bead, heat shield (no, yes) responses: measured temperature of a heated plate
• variables: voltage, warm-up procedure, bulb age responses: sensitivity of micro densitometer
• variables: pressure, amount of ferric chloride added, amount of lime added responses: efficiency of vacuum filtration of sludge
• variables: longitudinal feed rate, transverse feed rate, depth of cut responses: longitudinal and thrust forces for surface grinding operation
• variables: time between preparation of sample and refluxing, reflux time, time between end of reflux and start of titrating responses: chemical oxygen demand of samples with same amount of waste (acetanilide)
• variables: speed of rotation, thrust load, method of lubrication responses: torque of taper roller bearings
• variables: type of activated carbon, amount of carbon, pH responses: adsorption characteristics of activated carbon used with municipal waste water
• variables: amounts of nickel, manganese, carbon responses: impact strength of steel alloy
• variables: form (broth, gravy), added broth (no, yes), added fat (no, yes), type of meat (lamb, beef) responses: percentage of panelists correctly identifying which samples were lamb
• variables: well (A, B), depth of probe, method of analysis (peak height, planimeter) responses: methane concentration in completed sanitary landfill
• variables: paste (A, B), preparation of skin (no, yes), site (sternum, forearm) responses: electrocardiogram reading
• variables: lime dosage, time of flocculation, mixing speed responses: removal of turbidity and hardness from water
• variables: temperature difference between surface and bottom waters, thickness of surface layer, jet distance to thermocline, velocity of jet, temperature difference between jet and bottom waters responses: mixing time for an initially thermally stratified tank of water

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Experimental Design: Types, Examples & Methods

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

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Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

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Experimental design refers to how participants are allocated to different groups in an experiment. Types of design include repeated measures, independent groups, and matched pairs designs.

Probably the most common way to design an experiment in psychology is to divide the participants into two groups, the experimental group and the control group, and then introduce a change to the experimental group, not the control group.

The researcher must decide how he/she will allocate their sample to the different experimental groups.  For example, if there are 10 participants, will all 10 participants participate in both groups (e.g., repeated measures), or will the participants be split in half and take part in only one group each?

Three types of experimental designs are commonly used:

1. Independent Measures

Independent measures design, also known as between-groups , is an experimental design where different participants are used in each condition of the independent variable.  This means that each condition of the experiment includes a different group of participants.

This should be done by random allocation, ensuring that each participant has an equal chance of being assigned to one group.

Independent measures involve using two separate groups of participants, one in each condition. For example:

• Con : More people are needed than with the repeated measures design (i.e., more time-consuming).
• Pro : Avoids order effects (such as practice or fatigue) as people participate in one condition only.  If a person is involved in several conditions, they may become bored, tired, and fed up by the time they come to the second condition or become wise to the requirements of the experiment!
• Con : Differences between participants in the groups may affect results, for example, variations in age, gender, or social background.  These differences are known as participant variables (i.e., a type of extraneous variable ).
• Control : After the participants have been recruited, they should be randomly assigned to their groups. This should ensure the groups are similar, on average (reducing participant variables).

2. Repeated Measures Design

Repeated Measures design is an experimental design where the same participants participate in each independent variable condition.  This means that each experiment condition includes the same group of participants.

Repeated Measures design is also known as within-groups or within-subjects design .

• Pro : As the same participants are used in each condition, participant variables (i.e., individual differences) are reduced.
• Con : There may be order effects. Order effects refer to the order of the conditions affecting the participants’ behavior.  Performance in the second condition may be better because the participants know what to do (i.e., practice effect).  Or their performance might be worse in the second condition because they are tired (i.e., fatigue effect). This limitation can be controlled using counterbalancing.
• Pro : Fewer people are needed as they participate in all conditions (i.e., saves time).
• Control : To combat order effects, the researcher counter-balances the order of the conditions for the participants.  Alternating the order in which participants perform in different conditions of an experiment.

Counterbalancing

Suppose we used a repeated measures design in which all of the participants first learned words in “loud noise” and then learned them in “no noise.”

We expect the participants to learn better in “no noise” because of order effects, such as practice. However, a researcher can control for order effects using counterbalancing.

The sample would be split into two groups: experimental (A) and control (B).  For example, group 1 does ‘A’ then ‘B,’ and group 2 does ‘B’ then ‘A.’ This is to eliminate order effects.

Although order effects occur for each participant, they balance each other out in the results because they occur equally in both groups.

3. Matched Pairs Design

A matched pairs design is an experimental design where pairs of participants are matched in terms of key variables, such as age or socioeconomic status. One member of each pair is then placed into the experimental group and the other member into the control group .

One member of each matched pair must be randomly assigned to the experimental group and the other to the control group.

• Con : If one participant drops out, you lose 2 PPs’ data.
• Pro : Reduces participant variables because the researcher has tried to pair up the participants so that each condition has people with similar abilities and characteristics.
• Con : Very time-consuming trying to find closely matched pairs.
• Pro : It avoids order effects, so counterbalancing is not necessary.
• Con : Impossible to match people exactly unless they are identical twins!
• Control : Members of each pair should be randomly assigned to conditions. However, this does not solve all these problems.

Experimental design refers to how participants are allocated to an experiment’s different conditions (or IV levels). There are three types:

1. Independent measures / between-groups : Different participants are used in each condition of the independent variable.

2. Repeated measures /within groups : The same participants take part in each condition of the independent variable.

3. Matched pairs : Each condition uses different participants, but they are matched in terms of important characteristics, e.g., gender, age, intelligence, etc.

Learning Check

Read about each of the experiments below. For each experiment, identify (1) which experimental design was used; and (2) why the researcher might have used that design.

1 . To compare the effectiveness of two different types of therapy for depression, depressed patients were assigned to receive either cognitive therapy or behavior therapy for a 12-week period.

The researchers attempted to ensure that the patients in the two groups had similar severity of depressed symptoms by administering a standardized test of depression to each participant, then pairing them according to the severity of their symptoms.

2 . To assess the difference in reading comprehension between 7 and 9-year-olds, a researcher recruited each group from a local primary school. They were given the same passage of text to read and then asked a series of questions to assess their understanding.

3 . To assess the effectiveness of two different ways of teaching reading, a group of 5-year-olds was recruited from a primary school. Their level of reading ability was assessed, and then they were taught using scheme one for 20 weeks.

At the end of this period, their reading was reassessed, and a reading improvement score was calculated. They were then taught using scheme two for a further 20 weeks, and another reading improvement score for this period was calculated. The reading improvement scores for each child were then compared.

4 . To assess the effect of the organization on recall, a researcher randomly assigned student volunteers to two conditions.

Condition one attempted to recall a list of words that were organized into meaningful categories; condition two attempted to recall the same words, randomly grouped on the page.

Experiment Terminology

Ecological validity.

The degree to which an investigation represents real-life experiences.

Experimenter effects

These are the ways that the experimenter can accidentally influence the participant through their appearance or behavior.

Demand characteristics

The clues in an experiment lead the participants to think they know what the researcher is looking for (e.g., the experimenter’s body language).

Independent variable (IV)

The variable the experimenter manipulates (i.e., changes) is assumed to have a direct effect on the dependent variable.

Dependent variable (DV)

Variable the experimenter measures. This is the outcome (i.e., the result) of a study.

Extraneous variables (EV)

All variables which are not independent variables but could affect the results (DV) of the experiment. Extraneous variables should be controlled where possible.

Confounding variables

Variable(s) that have affected the results (DV), apart from the IV. A confounding variable could be an extraneous variable that has not been controlled.

Random Allocation

Randomly allocating participants to independent variable conditions means that all participants should have an equal chance of taking part in each condition.

The principle of random allocation is to avoid bias in how the experiment is carried out and limit the effects of participant variables.

Order effects

Changes in participants’ performance due to their repeating the same or similar test more than once. Examples of order effects include:

(i) practice effect: an improvement in performance on a task due to repetition, for example, because of familiarity with the task;

(ii) fatigue effect: a decrease in performance of a task due to repetition, for example, because of boredom or tiredness.

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Title: fair clustering: critique, caveats, and future directions.

Abstract: Clustering is a fundamental problem in machine learning and operations research. Therefore, given the fact that fairness considerations have become of paramount importance in algorithm design, fairness in clustering has received significant attention from the research community. The literature on fair clustering has resulted in a collection of interesting fairness notions and elaborate algorithms. In this paper, we take a critical view of fair clustering, identifying a collection of ignored issues such as the lack of a clear utility characterization and the difficulty in accounting for the downstream effects of a fair clustering algorithm in machine learning settings. In some cases, we demonstrate examples where the application of a fair clustering algorithm can have significant negative impacts on social welfare. We end by identifying a collection of steps that would lead towards more impactful research in fair clustering.

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