controlled experiment drawing

Microbe Notes

Controlled Experiments: Definition, Steps, Results, Uses

Controlled experiments ensure valid and reliable results by minimizing biases and controlling variables effectively.

Rigorous planning, ethical considerations, and precise data analysis are vital for successful experiment execution and meaningful conclusions.

Real-world applications demonstrate the practical impact of controlled experiments, guiding informed decision-making in diverse domains.

Controlled experiments are the systematic research method where variables are intentionally manipulated and controlled to observe the effects of a particular phenomenon. It aims to isolate and measure the impact of specific variables, ensuring a more accurate causality assessment.

Table of Contents

Interesting Science Videos

Importance of controlled experiments in various fields

Controlled experiments are significant across diverse fields, including science, psychology, economics, healthcare, and technology.

They provide a systematic approach to test hypotheses, establish cause-and-effect relationships, and validate the effectiveness of interventions or solutions.

Why Controlled Experiments Matter?

Validity and reliability of results.

Controlled experiments uphold the gold standard for scientific validity and reliability. By meticulously controlling variables and conditions, researchers can attribute observed outcomes accurately to the independent variable being tested. This precision ensures that the findings can be replicated and are trustworthy.

Minimizing Biases and Confounding Variables

One of the core benefits of controlled experiments lies in their ability to minimize biases and confounding variables. Extraneous factors that could distort results are mitigated through careful control and randomization. This enables researchers to isolate the effects of the independent variable, leading to a more accurate understanding of causality.

Achieving Causal Inference

Controlled experiments provide a strong foundation for establishing causal relationships between variables. Researchers can confidently infer causation by manipulating specific variables and observing resulting changes. The capability informs decision-making, policy formulation, and advancements across various fields.

Planning a Controlled Experiment

Formulating research questions and hypotheses.

Formulating clear research questions and hypotheses is paramount at the outset of a controlled experiment. These inquiries guide the direction of the study, defining the variables of interest and setting the stage for structured experimentation.

Well-defined questions and hypotheses contribute to focused research and facilitate meaningful data collection.

Identifying Variables and Control Groups

Identifying and defining independent, dependent, and control variables is fundamental to experimental planning.

Precise identification ensures that the experiment is designed to isolate the effect of the independent variable while controlling for other influential factors. Establishing control groups allows for meaningful comparisons and robust analysis of the experimental outcomes.

Designing Experimental Procedures and Protocols

Careful design of experimental procedures and protocols is essential for a successful controlled experiment. The step involves outlining the methodology, data collection techniques, and the sequence of activities in the experiment.

A well-designed experiment is structured to maintain consistency, control, and accuracy throughout the study, thereby enhancing the validity and credibility of the results.

Conducting a Controlled Experiment

Randomization and participant selection.

Randomization is a critical step in ensuring the fairness and validity of a controlled experiment. It involves assigning participants to different experimental conditions in a random and unbiased manner.

The selection of participants should accurately represent the target population, enhancing the results’ generalizability.

Data Collection Methods and Instruments

Selecting appropriate data collection methods and instruments is pivotal in gathering accurate and relevant data. Researchers often employ surveys, observations, interviews, or specialized tools to record and measure the variables of interest.

The chosen methods should align with the experiment’s objectives and provide reliable data for analysis.

Monitoring and Maintaining Experimental Conditions

Maintaining consistent and controlled experimental conditions throughout the study is essential. Regular monitoring helps ensure that variables remain constant and uncontaminated, reducing the risk of confounding factors.

Rigorous monitoring protocols and timely adjustments are crucial for the accuracy and reliability of the experiment.

Analysing Results and Drawing Conclusions

Data analysis techniques.

Data analysis involves employing appropriate statistical and analytical techniques to process the collected data. This step helps derive meaningful insights, identify patterns, and draw valid conclusions.

Common techniques include regression analysis, t-tests , ANOVA , and more, tailored to the research design and data type .

Interpretation of Results

Interpreting the results entails understanding the statistical outcomes and their implications for the research objectives.

Researchers analyze patterns, trends, and relationships revealed by the data analysis to infer the experiment’s impact on the variables under study. Clear and accurate interpretation is crucial for deriving actionable insights.

Implications and Potential Applications

Identifying the broader implications and potential applications of the experiment’s results is fundamental. Researchers consider how the findings can inform decision-making, policy development, or further research.

Understanding the practical implications helps bridge the gap between theoretical insights and real-world application.

Common Challenges and Solutions

Addressing ethical considerations.

Ethical challenges in controlled experiments include ensuring informed consent, protecting participants’ privacy, and minimizing harm.

Solutions involve thorough ethics reviews, transparent communication with participants, and implementing safeguards to uphold ethical standards throughout the experiment.

Dealing with Sample Size and Statistical Power

The sample size is crucial for achieving statistically significant results. Adequate sample sizes enhance the experiment’s power to detect meaningful effects accurately.

Statistical power analysis guides researchers in determining the optimal sample size for the experiment, minimizing the risk of type I and II errors .

Mitigating Unforeseen Variables

Unforeseen variables can introduce bias and affect the experiment’s validity. Researchers employ meticulous planning and robust control measures to minimize the impact of unforeseen variables.

Pre-testing and pilot studies help identify potential confounders, allowing researchers to adapt the experiment accordingly.

A controlled experiment involves meticulous planning, precise execution, and insightful analysis. Adhering to ethical standards, optimizing sample size, and adapting to unforeseen variables are key challenges that require thoughtful solutions.

Real-world applications showcase the transformative potential of controlled experiments across varied domains, emphasizing their indispensable role in evidence-based decision-making and progress.

https://www.khanacademy.org/science/biology/intro-to-biology/science-of-biology/a/experiments-and-observations
https://www.scribbr.com/methodology/controlled-experiment/
https://link.springer.com/10.1007/978-1-4899-7687-1_891
http://ai.stanford.edu/~ronnyk/GuideControlledExperiments.pdf
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6776925/
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4017459/
https://www.merriam-webster.com/dictionary/controlled%20experiment

About Author

Krisha Karki

Controlled Experiment

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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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.

This is when a hypothesis is scientifically tested.

In a controlled experiment, an independent variable (the cause) is systematically manipulated, and the dependent variable (the effect) is measured; any extraneous variables are controlled.

The researcher can operationalize (i.e., define) the studied variables so they can be objectively measured. The quantitative data can be analyzed to see if there is a difference between the experimental and control groups.

What is the control group?

In experiments scientists compare a control group and an experimental group that are identical in all respects, except for one difference – experimental manipulation.

Unlike the experimental group, the control group is not exposed to the independent variable under investigation and so provides a baseline against which any changes in the experimental group can be compared.

Since experimental manipulation is the only difference between the experimental and control groups, we can be sure that any differences between the two are due to experimental manipulation rather than chance.

Randomly allocating participants to independent variable groups means that all participants should have an equal chance of participating 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.

What are extraneous variables?

The researcher wants to ensure that the manipulation of the independent variable has changed the changes in the dependent variable.

Hence, all the other variables that could affect the dependent variable to change must be controlled. These other variables are called extraneous or confounding variables.

Extraneous variables should be controlled were possible, as they might be important enough to provide alternative explanations for the effects.

controlled experiment extraneous variables

In practice, it would be difficult to control all the variables in a child’s educational achievement. For example, it would be difficult to control variables that have happened in the past.

A researcher can only control the current environment of participants, such as time of day and noise levels.

Why conduct controlled experiments?

Scientists use controlled experiments because they allow for precise control of extraneous and independent variables. This allows a cause-and-effect relationship to be established.

Controlled experiments also follow a standardized step-by-step procedure. This makes it easy for another researcher to replicate the study.

Key Terminology

Experimental group.

The group being treated or otherwise manipulated for the sake of the experiment.

Control Group

They receive no treatment and are used as a comparison group.

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 that 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 participating in each condition.

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.

What is the control in an experiment?

In an experiment , the control is a standard or baseline group not exposed to the experimental treatment or manipulation. It serves as a comparison group to the experimental group, which does receive the treatment or manipulation.

The control group helps to account for other variables that might influence the outcome, allowing researchers to attribute differences in results more confidently to the experimental treatment.

Establishing a cause-and-effect relationship between the manipulated variable (independent variable) and the outcome (dependent variable) is critical in establishing a cause-and-effect relationship between the manipulated variable.

What is the purpose of controlling the environment when testing a hypothesis?

Controlling the environment when testing a hypothesis aims to eliminate or minimize the influence of extraneous variables. These variables other than the independent variable might affect the dependent variable, potentially confounding the results.

By controlling the environment, researchers can ensure that any observed changes in the dependent variable are likely due to the manipulation of the independent variable, not other factors.

This enhances the experiment’s validity, allowing for more accurate conclusions about cause-and-effect relationships.

It also improves the experiment’s replicability, meaning other researchers can repeat the experiment under the same conditions to verify the results.

Why are hypotheses important to controlled experiments?

Hypotheses are crucial to controlled experiments because they provide a clear focus and direction for the research. A hypothesis is a testable prediction about the relationship between variables.

It guides the design of the experiment, including what variables to manipulate (independent variables) and what outcomes to measure (dependent variables).

The experiment is then conducted to test the validity of the hypothesis. If the results align with the hypothesis, they provide evidence supporting it.

The hypothesis may be revised or rejected if the results do not align. Thus, hypotheses are central to the scientific method, driving the iterative inquiry, experimentation, and knowledge advancement process.

What is the experimental method?

The experimental method is a systematic approach in scientific research where an independent variable is manipulated to observe its effect on a dependent variable, under controlled conditions.

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Understanding Controlled Experiments

1. introduction: the scientific method.

The scientific method is typically taught as a step-by-step sequence. Drag the steps below, listed in alphabetical order, into an order that matches the steps described in the table.

[qwiz style=”width: 700px !important; min-height: 400px !important;”]

[h] Steps of the Scientific Method


	This is where it begins: Sensing the world, and noticing patterns and relationships
	This stage involves making an educated guess that includes a prediction,
	This phase involves a structured form of observation that allows you to examine one thing at a time.
	This last stage involves answering questions such as 1) Was the hypothesis correct? 2) Are there other lines of evidence that point in the same direction?

[l] Drawing conclusions

[f*] Correct!

[fx] No. Please try again.

[l] Formulating hypotheses

[l] Making observations

[f*] Excellent!

[fx] No, that’s not correct. Please try again.

[l] Performing experiments

[f*] Great!

2. Interactive Reading: A Case Study: The link between cancer and smoking. Initial observations

To learn about the scientific method and experimentation, we’ll look at a very simplified history of the discovery of the link between smoking tobacco and cancer. 1

1 For a detailed view of this story, follow the links to tobaccocontrol.bmj.com at the end of this tutorial. Much of the information below comes from that site.

[qwiz qrecord_id=”sciencemusicvideosMeister1961-Controlled Experiments 1: Cancer and Smoking 1″]

[h]Interactive Reading: The Link between Cancer and Smoking

[i]Carefully read what follows, dragging in the words on top to the right place.

[q labels = “top”]

It hasn’t always been known that smoking tobacco caused lung cancer. In the 1500s, tobacco was praised for its supposed health benefits. Lung cancer itself was once extremely _______ . But mechanical production of cigarettes, free distribution of cigarettes to soldiers, and mass marketing caused a global lung cancer ____________ that began in the 1900s and continues today.

The first observations of the connection started around 1900. The key observation was the rise in _______ cancer rates. Among the first to notice this connection was a German medical student, Hermann Rottman, who noticed higher rates of lung cancer among German __________ workers. Rottman suspected that exposure to tobacco dust was causing cancer.

[l] epidemic

[l] tobacco

By the 1920s, the increasing rate of lung cancer began to be linked with _________ , but other possible causes for increased lung cancer rates were also considered. These included exposure to poison gas suffered by soldiers during World War One and exposure to the tar that was increasingly used on roads as driving became more common.

In the 1930s, population studies in German hospitals led to the discovery that lung cancer patients were far ___________ to have smoked than patients who didn’t have cancer. By the 1950s, American doctors were able to calculate that “smokers of 35 cigarettes per day increased their odds of ________ from lung cancer by a factor of 40.” (tobaccocontrol.bmj.com).

So, by that point there was a clear __________ : if someone smokes, then they have a higher chance of developing lung cancer. Now let’s look at how experiments could be designed to confirm that hypothesis.

[l] hypothesis

[l] more likely

[l] smoking

3. Controlled Experiments: General Features

For the sake of simplicity (and learning), the experiment described below is somewhat different from the actual animal experiments that were performed to help establish the link between tobacco smoke and cancer.

Let’s start by reviewing what an experiment is: it’s a controlled form of observation that lets you observe one thing at a time. As you read what follows, refer to the diagram below.

[qwiz style = “width: 528px; min-height:0px; border: 3px solid black; ” qrecord_id=”sciencemusicvideosMeister1961-Controlled Experiments: General Features”]

[h]Quiz: Controlled Experiments, General Features

[q] Experiments try to test the effect of ONE thing at a time. The thing that you test is called the independent variable.

In relationship to our hypothesis ( if someone smokes, they have a higher chance of developing lung cancer) , what’s the independent variable?

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[q]Because we’re testing a harmful substance, we’re not going to test humans, but animals related to humans (like mice or rats). Keep that in mind when we talk about “groups” and “individuals” below.

the standard for comparison.
not exposed to the independent variable.
The second group is the experimental group . This group gets exposed to the independent variable.

In relationship to our hypothesis about smoking and cancer, what will be our control group?

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[q] What’s the experimental group?

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[q]An experiment is going to have some observable outcome. That outcome is called the dependent variable. In relationship to our hypothesis ( if someone smokes, they have a higher chance of developing lung cancer) , what’s the dependent variable?

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4. Scientific Method and Experimental Design Flashcards

To make sure you understand the key terms we’ve used in this lesson, work through these flashcards. Flashcards can feel very difficult, but they’re incredibly effective in helping you to remember what you’ve learned. Be very honest with yourself as you use these cards. It’s much better to study a card twice than to rush through without learning the material.

Click here to start flashcard deck [qdeck style=”width: 528px; border: 2px solid black; ” qrecord_id=”sciencemusicvideosMeister1961-Controlled Experiments Flashcards”]

[h] Flashcards: The Scientific Method and Controlled Experiments

[i] Instructions.

Click ‘Flip’ to see the answer to each card.
If you know it, click ‘Got it.”
If you don’t know it as well as you’d like, click ‘Need more practice,’ and that card will go to the bottom of the deck so you can practice it again.
‘Shuffle’ lets you shuffle the deck.

[!]Card 1++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++[/!]

[q] The step in the scientific method that involves sensing the world, and noticing patterns and relationships is [textentry]

[a]The step in the scientific method that involves sensing the world, and noticing patterns and relationships is observation.

[!] CARD 2++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++[/!]

[q]An educated guess that includes a prediction is a _______________

[textentry] [a]An educated guess that includes a prediction is a hypothesis .

[!] CARD 3++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++[/!]

[q]A structured form of observation that allows you to examine one thing at a time is a(n) _______________

[textentry] [a]A structured form of observation that allows you to examine one thing at a time is a(n) experiment

[!] CARD 5++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++[/!]

[q]An experiment tests the validity (or correctness) of a(n) _______________

[textentry] [a]An experiment tests the validity of a hypothesis .

[!] CARD 6++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++[/!]

[q]A well-formulated hypothesis includes a __________

[textentry] [a]A well-formulated hypothesis includes a prediction

[!] CARD 7++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++[/!]

[q]The thing you test in an experiment is the _________

[textentry] [a]The thing you test in an experiment is the independent variable

[!] CARD 8++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++[/!]

[q]The measured or observed result of the independent variable is the ___________

[textentry] [a]The measured or observed result of the independent variable is the dependent variable.

[!] CARD 9++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++[/!]

[q]A student performs an experiment to test the effect of red light on plant growth. The independent variable is ________

[textentry]

[a]A student performs an experiment to test the effect of red light on plant growth. The independent variable is red light

[!] CARD 10++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++[/!]

[q]A student performs an experiment to test the effect of red light on plant growth. The student’s hypothesis is that red light will produce more growth than normal light. What would be a logical control group?

[a]A student performs an experiment to test the effect of red light on plant growth. The student’s hypothesis is that red light will produce more growth than normal light. A logical control group would be plants exposed to normal light.

[!] CARD 11++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++[/!]

[a]A student performs an experiment to test the effect of red light on plant growth. The student’s hypothesis is that red light will produce more growth than normal light. A logical experimental group would be plants exposed to red light.

If you want more practice, please press the restart button below. Otherwise, follow the links below. [restart] [/qdeck]

5. A controlled experiment to test the smoking/cancer connection

So, how would this work in the case of an animal experiment to test the hypothesis that tobacco smoke causes cancer?

Well, if it’s an animal experiment, we need an animal.

Like humans, rats are mammals. Their internal organs, including their lungs, look very much like miniature versions of those in humans. In terms of body chemistry, they’re also very much like us: many of the chemical reactions occurring in our cells are identical. So the reasoning (which is widely accepted in biology) is that if something causes cancer in a rat, it is likely to cause cancer in a human being.

[qwiz style = “width: 528px; min-height:0px; border: 3px solid black; ” qrecord_id=”sciencemusicvideosMeister1961-Controlled Experiments: Quiz 2″]

[h]Using the Scientific method

[q]So here’s our experiment. We’re going to have two rats. In one group, we’ll have a rat that smokes. In the second group, we’ll have a rat that’s exposed to exactly the same conditions (the same food, temperature, etc.). The only difference is that the second rat won’t smoke.


a non-smoking rat	a smoking rat

What’s the problem with this experiment?

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[q]So, here’s what we’re going to do: We’re going to use two groups of rats, each with enough individuals to show the effect of the independent variable. One group will be exposed to tobacco smoke. A second group will be kept under identical conditions, except for the fact that it won’t be exposed to tobacco smoke. We’re going to measure the rate of cancer in each group, and see if there’s a difference.

If there is a difference, we can be pretty sure that it’s a result of the presence of the independent variable (tobacco smoke). This difference, because it depends on the effect of the independent variable, is called the dependent variable.

What’s the control group in this experiment?

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[c]VG9iYWNjbyBzbW9rZS4=[Qq]

[c]VGhlIHJhdGUgb2YgbHVuZyBjYW5jZXIgaW4gdGhlIGNvbnRyb2wgYW5kIGV4cGVyaW1lbnRhbCBncm91cC4=[Qq]

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[f]Tm8uIFRvYmFjY28gc21va2UgaXMgdGhlIGluZGVwZW5kZW50IHZhcmlhYmxlLg==[Qq]

[f]Tm8uIFRoZSByYXRlIG9mIGx1bmcgY2FuY2VywqBpcyB0aGUgZGVwZW5kZW50wqB2YXJpYWJsZS4=[Qq]

What’s the experimental group in this experiment?

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What’s the dependent variable in this experiment?

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[q]What’s the independent variable in this experiment?

[c]VG9iYWNjby BzbW9rZS4=[Qq]

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6. What Happened Next

Animal studies like the one described above confirmed the hypothesis that rats exposed to tobacco smoke will have higher rates of cancer. In fact, this had been known since the 1930s, when controlled experiments showed that elements of tobacco smoke, when put into liquid form, could cause tumors to form on the skin of rabbits.

Throughout the 1900s, several lines of evidence confirmed the tobacco/cancer link. These lines of evidence included

Chemical analysis of tobacco to identify cancer-causing agents,
Studies of how cells in lung tissue were affected by smoking, and
Public health studies showing that people who smoked were more likely to develop lung cancer.

However, through much of the 1900s, smoking continued to increase among many populations around the world. This was largely caused by tobacco companies, which continued to market cigarettes, and which devoted significant amounts of resources to denying the scientific evidence about the danger of smoking. You can read the entire story by following the links at the bottom of this tutorial.

7. Checking Understanding Quiz

In this tutorial, we’ve learned about

Hypothesize
Draw conclusions
independent variable
dependent variable
control group
experimental group
It tests only one thing (the independent variable)
It uses large enough groups to avoid random results based on individual differences.

To make sure you’ve mastered this material, take the quiz below.

[qwiz style = “border: 3px solid black; ” qrecord_id=”sciencemusicvideosMeister1961-Controlled Experiments: Checking Understanding”]

[h]Quiz: The Scientific Method and Designing Experiments [i] Here’s how the quiz works:

Each question is multiple-choice, but the entire quiz is like a series of flashcards.
If you get the question right, it comes off the deck.
If you get the question wrong, it goes to the bottom of the deck, so you can try it again.

[q] Noticing patterns in the world around you is best classified as

[c]bWFraW5nIGFuIG 9ic2VydmF0aW9u[Qq]

[c]YXNraW5nIGEgcXVlc3Rpb24=[Qq]

[c]Zm9ybXVsYXRpbmcgYSBoeXBvdGhlc2lz[Qq]

[c]cGVyZm9ybWluZyBhbiBleHBlcmltZW50[Qq]

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[q] An educated guess that includes a prediction is a(n)

[c]IG9ic2VydmF0aW9u[Qq]

[c]wqBoeXBv dGhlc2lz[Qq]

[c]IGV4cGVyaW1lbnQ=[Qq]

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[q] A structured form of observation that allows you to observe one thing at a time is a(n)

[c]wqBoeXBvdGhlc2lz[Qq]

[c]IGV4cGVy aW1lbnQ=[Qq]

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[q] This includes a prediction, and is best put in an “if…then…” format

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[q] This part of the scientific method involves testing whether a hypothesis is correct.

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[q]In an experiment, the thing you’re testing is the

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[q]In an experiment, the measured result is the

[c]aW5kZXBlbmRlbnQgdmFyaWFibGU=[Qq]

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[q]The part of the experiment that serves as a standard for comparison, and which shows you what the result would be without the independent variable, is the

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[f]Tm8uwqBUaGUgaHlwb3RoZXNpcyBpcyB0aGUgZWR1Y2F0ZWQgZ3Vlc3MvcHJlZGljdGlvbiB0aGF0IHlvdXIgZXhwZXJpbWVudCBpcyB0cnlpbmcgdG8gcHJvdmUgKG9yIGRpc3Byb3ZlKQ==[Qq] [f]Tm8uwqBUaGUgZGVwZW5kZW50IHZhcmlhYmxlIGlzIHRoZSByZXN1bHQgeW91IGdldC4=[Qq] [f]WWVzLiBUaGUgY29udHJvbCBncm91cCBpcyB0aGXCoHN0YW5kYXJkIGZvciBjb21wYXJpc29uLiBJdCBzaG93cyB5b3Ugd2hhdCB0aGUgcmVzdWx0IHdvdWxkIGJlIHdpdGhvdXQgdGhlIGluZGVwZW5kZW50IHZhcmlhYmxlLg==

[f]Tm8uIFRoZSBleHBlcmltZW50YWzCoGdyb3VwIGlzIHRoZSBncm91cCB0aGF0IGlzIGV4cG9zZWQgdG8gdGhlIGluZGVwZW5kZW50IHZhcmlhYmxlLsKgV2hhdCYjODIxNztzIHRoZSBncm91cCB0aGF0IGRvZXNuJiM4MjE3O3QgZ2V0IGV4cG9zZWQgdG8gdGhlIGluZGVwZW5kZW50IHZhcmlhYmxlPw==

[q]Two students have designed an experiment to test the effect of loud bass notes on reproduction rates in guppies (a small aquarium fish). They divide the guppies into two groups of 15, each group in its own 20-gallon aquarium tank. One tank is exposed to the loud bass notes, and one is not.

In this experiment, which of the following is the independent variable?

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[c]dGhlIGd1cHBpZXMgTk9UwqBleHBvc2VkIHRvIHRoZSBsb3fCoGJhc3Mgbm90ZXMu[Qq]

[c]dGhlIGxvd8KgYm FzcyBub3Rlcy4=[Qq]

[c]dGhlIHJhdGUgb2YgcmVwcm9kdWN0aW9u[Qq]

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[f]Tm8uIFRoZSBpbmRlcGVuZGVudCB2YXJpYWJsZSBpcyB0aGUgdGhpbmcgeW91JiM4MjE3O3JlIHRlc3RpbmcuIFdoYXQmIzgyMTc7cyBiZWluZyB0ZXN0ZWQ/

[q]Two students have designed an experiment to test the effect of low bass notes on reproduction rates in guppies (a small aquarium fish). They divide the guppies into two groups of 15, each group in its own 20-gallon aquarium tank. One tank is exposed to the low bass notes, and one is not.

In this experiment, which of the following is the dependent variable?

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[c]dGhlIGd1cHBpZXMgTk9UwqBleHBvc2VkIHRvIHRoZSBsb3fCoGJhc3Mgbm90ZXM=[Qq]

[c]dGhlIGxvd8KgYmFzcyBub3Rlcw==[Qq]

[c]dGhlIHJhdGUgb2Yg cmVwcm9kdWN0aW9u[Qq]

[f]Tm8uwqBUaGUgZGVwZW5kZW50IHZhcmlhYmxlIGlzIHRoZSByZXN1bHQgeW91IGdldC4gVGhlc2UgZXhwZXJpbWVudGVycyB3YW50IHRvIHNlZSBpZiBsb3cgYmFzcyBub3Rlc8KgYWZmZWN0IHNvbWV0aGluZy4gV2hhdCBpcyB0aGF0IHNvbWV0aGluZz8=[Qq] [f]Tm8uwqBUaGUgZGVwZW5kZW50IHZhcmlhYmxlIGlzIHRoZSByZXN1bHQgeW91IGdldC4gVGhlc2UgZXhwZXJpbWVudGVycyB3YW50IHRvIHNlZSBpZiBsb3cgYmFzcyBub3Rlc8KgYWZmZWN0IHNvbWV0aGluZy4gV2hhdCBpcyB0aGF0IHNvbWV0aGluZz8=[Qq] [f]Tm8uwqBUaGUgZGVwZW5kZW50IHZhcmlhYmxlIGlzIHRoZSByZXN1bHQgeW91IGdldC4gVGhlc2UgZXhwZXJpbWVudGVycyB3YW50IHRvIHNlZSBpZiBsb3cgYmFzZSBub3RlcyBhZmZlY3Qgc29tZXRoaW5nLiBXaGF0IGlzIHRoYXQgc29tZXRoaW5nPw==

[f]WWVzLsKgVGhlIGRlcGVuZGVudCB2YXJpYWJsZSBpcyB0aGUgcmVzdWx0IHlvdSBnZXQuIFRoZXNlIGV4cGVyaW1lbnRlcnMgd2FudCB0byBzZWUgaWYgbG93IGJhc3PCoG5vdGVzIGFmZmVjdCByZXByb2R1Y3Rpb24gcmF0ZXMuIFRoZXJlZm9yZSByZXByb2R1Y3Rpb24gcmF0ZXMgYXJlIHRoZSBpbmRlcGVuZGVudCB2YXJpYWJsZS4=

In this experiment, which of the following is the control group ?

[c]dGhlIGd1cHBpZXMgZXhwb3NlZCB0byB0aGUgbG93IGJhc3Mgbm90ZXM=[Qq]

[c]dGhlIGd1cHBpZXMgTk9UwqBleHBvc2Vk IHRvIHRoZSBsb3cgYmFzcyBub3Rlcw==[Qq]

[c]dGhlIGxvdyBiYXNzIG5vdGVz[Qq]

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In this experiment, which of the following is the experimental group ?

[c]dGhlIGd1cHBpZXMgZXhwb3NlZCB0 byB0aGUgbG93IGJhc3Mgbm90ZXM=[Qq]

[f]WWVzLiBUaGUgZXhwZXJpbWVudGFswqBncm91cCBpcyB0aGUgZ3JvdXAgdGhhdCA= aXPCoGV4cG9zZWQgdG8gdGhlIGluZGVwZW5kZW50IHZhcmlhYmxlLsKg SW4gdGhpcyBleHBlcmltZW50LCB0aGUgaW5kZXBlbmRlbnQgdmFyaWFibGUgaXMgdGhlIGxvd8KgYmFzcyBub3RlcywgYW5kIHRoZSBndXBwaWVzIGV4cG9zZWQgdG8gdGhpcyBzb3VuZCBtYWtlIHVwIHRoZSBleHBlcmltZW50YWwgZ3JvdXAu[Qq] [f]Tm8uIFRoZSBleHBlcmltZW50YWzCoGdyb3VwIGlzIHRoZSBncm91cCB0aGF0IA== aXMgZXhwb3NlZCB0byB0aGUgaW5kZXBlbmRlbnQgdmFyaWFibGUuIEp1c3QgZmlndXJlIG91dCB3aGF0IHRoZSBpbmRlcGVuZGVudCB2YXJpYWJsZSBpcyAoaXQmIzgyMTc7cyB3aGF0IHlvdSYjODIxNztyZSB0ZXN0aW5nKSwgYW5kIHlvdSYjODIxNztsbCBoYXZlIHlvdXIgYW5zd2VyLsKg [Qq] [f]Tm8uIFRoZSBleHBlcmltZW50YWzCoGdyb3VwIGlzIHRoZSBncm91cCB0aGF0IA== aXMgZXhwb3NlZCB0byB0aGUgaW5kZXBlbmRlbnQgdmFyaWFibGUuIEp1c3QgZmlndXJlIG91dCB3aGF0IHRoZSBpbmRlcGVuZGVudCB2YXJpYWJsZSBpcyAoaXQmIzgyMTc7cyB3aGF0IHlvdSYjODIxNztyZSB0ZXN0aW5nKSwgYW5kIHlvdSYjODIxNztsbCBoYXZlIHlvdXIgYW5zd2VyLg==

[f]Tm8uIFRoZSBleHBlcmltZW50YWzCoGdyb3VwIGlzIHRoZSBncm91cCB0aGF0IA== aXMgZXhwb3NlZCB0byB0aGUgaW5kZXBlbmRlbnQgdmFyaWFibGUuIEp1c3QgZmlndXJlIG91dCB3aGF0IHRoZSBpbmRlcGVuZGVudCB2YXJpYWJsZSBpcyAoaXQmIzgyMTc7cyB3aGF0IHlvdSYjODIxNztyZSB0ZXN0aW5nKSwgYW5kIHlvdSYjODIxNztsbCBoYXZlIHlvdXIgYW5zd2VyLg==

[q]Bob wants to test whether lemon juice can keep dandelion weeds from growing in his garden. He creates several solutions of lemon juice. He then takes dandelion seeds and sprouts them on paper towels. Each day, he sprays the same amount of each solution on the seeds. The data are shown above.

Solution	Number of Seeds	Percentage of seedlings that sprout
10% lemon juice	15	42%
30% lemon juice	15	44%
50% lemon juice	15	41%

Based on the data, what’s the problem with the design of Bob’s experiment

[c]SGUgaGFkIG5vIGRlcGVuZGVudCB2YXJpYWJsZQ==[Qq]

[c]SGUgaGFzIG1vcmUgdGhhbiBvbmXCoGluZGVwZW5kZW50IHZhcmlhYmxl[Qq]

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[f]Tm8uwqBUaGXCoGRlcGVuZGVudCB2YXJpYWJsZSBpcyB0aGUgb3V0Y29tZSwgYW5kIEJvYiYjODIxNztzIGV4cGVyaW1lbnQgaGFzIGEgY2xlYXIgb3V0Y29tZSAodGhlIHBlcmNlbnRhZ2Ugb2Ygc2VlZHMgc3Byb3V0aW5nKQ==[Qq] [f]Tm8uIEhlJiM4MjE3O3MgdGVzdGluZyB2YXJpb3VzIGNvbmNlbnRyYXRpb25zIG9mIHRoZSBzYW1lIHRoaW5nLCB3aGljaCBpcyBhIGZpbmUgcHJvY2VkdXJlLg==[Qq] [f]WWVzLsKgVGhlcmUmIzgyMTc7cyBubyBjb250cm9sIGdyb3VwLiBXaXRob3V0IGEgY29udHJvbCBncm91cCwgaXQmIzgyMTc7cyBpbXBvc3NpYmxlIHRvIGtub3cgd2hldGhlciBoYXZpbmcgYWJvdXQgNDAlIG9mIHNlZWRsaW5ncyBzcHJvdXRpbmcgaXMgYSByZXN1bHQgb2YgdGhlIGxlbW9uIGp1aWNlLCBvciB3aGV0aGVyIHRoYXQmIzgyMTc7cyBub3JtYWwgZm9yIGRhbmRlbGlvbiBzZWVkcy4=

[f]Tm8gKGJ1dCB0aGF0JiM4MjE3O3MgYSBzbWFydCByZXNwb25zZSkuIEl0IHRha2VzIGEgbG90IG9mIHN0YXRpc3RpY2FsIGtub3dsZWRnZSB0byBkZXRlcm1pbmUgdGhlIHJpZ2h0IHNhbXBsZSBzaXplLiAxNSBtaWdodCBiZSBva2F5LiBCdXQsIHRoZXJlJiM4MjE3O3MgYSBtdWNoIGJpZ2dlciBwcm9ibGVtIHdpdGggdGhpcyBleHBlcmltZW50Lg==

[q]Clara is testing whether iron pills will help skinny dogs gain weight. For her experiment, she takes three dogs, a poodle, a boxer, and a collie. She adds iron to their food for two weeks and then records their weight. Here are her results

Dog Breed	Number of 5-gram iron pills	Weight gain (kilograms)
Poodle	1	4
Boxer	3	2
Collie	5	6

Based on the data, what’s the problem with the design of her experiment

[c]U2hlIGhhZCBubyBkZXBlbmRlbnQgdmFyaWFibGU=[Qq]

[c]U2hlIGhhcyBtb3JlIHRoYW4gb25lwq BpbmRlcGVuZGVudCB2YXJpYWJsZQ==[Qq]

[c]U2hlIGhhcyBhIGNsZWFyIGJpYXMgdG93YXJkIENvbGxpZXMu[Qq]

[f]Tm8uwqBUaGXCoGRlcGVuZGVudCB2YXJpYWJsZSBpcyB0aGUgb3V0Y29tZSwgYW5kIENsYXJhJiM4MjE3O3PCoGV4cGVyaW1lbnQgaGFzIGEgY2xlYXIgb3V0Y29tZSAod2VpZ2h0IGdhaW4pLiBUaGluayBhYm91dCBob3cgbWFueSB0aGluZ3Mgc2hlJiM4MjE3O3MgdGVzdGluZy4=[Qq] [f]WWVzLiBTaGUmIzgyMTc7cyB1c2luZyBkb2dzIG9mIGEgZGlmZmVyZW50IGJyZWVkIGFuZCBnaXZpbmcgdGhlbSBhIGRpZmZlcmVudCBudW1iZXIgb2YgcGlsbHMsIHdoaWNoIG1lYW5zIHRoYXQgc2hlJiM4MjE3O3MgdGVzdGluZyBhdCBsZWFzdCB0d28gdGhpbmdzLg==[Qq] [f]Tm8uIFRoZXJlJiM4MjE3O3Mgbm8gZXZpZGVuY2XCoGZvciBjbGFpbWluZyB0aGF0IHNoZSYjODIxNztzIGJpYXNlZC4gQnV0IHRoaW5rIGFib3V0IGhvdyBtYW55IHRoaW5ncyBzaGUmIzgyMTc7cyB0ZXN0aW5nJiM4MjMwOw==

If you want to take this quiz again, click the button below

[/qwiz] If you need more practice, please scroll up to the top and work through this tutorial again. Otherwise, follow the links below:

8. The Scientific Method Song: Interactive Lyrics

This is an interactive reading of the lyrics to the Scientific Method Song . If you’re completing this tutorial on your own, and you want to watch the video, then click here (the link will open in a new tab). But if you’re in class, please check with your teacher first!

[qwiz qrecord_id=”sciencemusicvideosMeister1961-Controlled Experiments: Scientific Method Song, Interactive Lyrics”]

[h]Interactive Lyrics, Scientific Method Song

[q labels=”top”]

Science always begins with a ___________ Inspired by an ___________ , Next step, as you might surmise, Is to take your question and hypothesize,

A _____________ should include a prediction, An educated guess in a form of “if … then” Like if science rapping is a memory aid Then better retention will be displayed.

Now hypothesis set, you’re ready for next step Performance of an experiment, The independent variable’s what you __________ _____________ variable’s the result you get

[l] dependent

[l] observation

[l] question

[l] experiment

[l] hypothesize

[l] observe

Now science well-done’s about taking __________

Because clear results, that’s your ultimate goal.

To see if what you’re testing is the _______ of an effect,

Your design has to be perfect.

Use two groups: __________ and experimental: Experimental gets your independent variable Control group’s the same except for one move: The independent ____________ gets ______________ .

Like to prove tobacco smoke is a cause of cancer, A good experiment will bring you the answer, Take two groups of guinea pigs or rats to test And make the groups ______ , that’s statistically best

[l] control

[l] removed

[l] variable

You gotta make ’em big, cause there’s always random stuff, So a small group of ____________ is never enough! You can get cancer even though you’ve never smoked But a single __________ , well that’s just an anecdote.

So set up two cages exactly the same, Controlling variables is the name of the game, Experimental group to smoke gets exposed, Cause that’s the _______________ variable you proposed.

[l] independent

[l] subjects

[q] BRIDGE : But I’m not saying science is always the way to get to the _______ It won’t tell you whom to ______ or what path to pursue But amidst all this superstition and deceit It gives you a path to consult To cut through all the lies and confusion, And help you to come to your own __________

[l] conclusion

[l] difference

[l] examine

And sure it was second-hand smoke that you tested, So maybe your results will be ____________ . This happens to scientists all of the time, Whenever there’s a __________ in their design.

Last step: try to publish in a scientific ________________ , As you try to win science fame eternal. It’s a never-ending process, and it’s awfully demanding. But that’s how we build scientific ___________________

[l] contested

[l] journal

[l] understanding

9. Next steps (reading about “The Shameful Past…”)

Click the following link to read The Shameful Past: The history of the discovery of the cigarette-cancer link . ” This is the reading on page 3 of the student learning guide that goes with this module.
Return to the menu for Module 1: Biology, Core Concepts
Use the menu choices above to choose another module.

Biology Simple

Controlled Experiment

A controlled experiment is a scientific study where variables are carefully manipulated and controlled. It helps researchers establish cause-effect relationships.

In the realm of scientific research, controlled experiments hold significant importance for exploring and understanding various phenomena. By systematically adjusting and regulating specific variables, researchers can draw accurate conclusions and establish causal relationships. This methodical approach allows for the isolation of key factors influencing the outcomes, leading to reliable and reproducible results.

Through controlled experiments, scientists can unravel complex patterns, test hypotheses, and make informed decisions based on empirical evidence. In essence, controlled experiments serve as a cornerstone in the scientific method, providing a structured framework for inquiry and discovery.

Credit: www.simplypsychology.org

Designing A Controlled Experiment

In the process of conducting a controlled experiment, designing plays a pivotal role in setting the stage for accurate and reliable results. Each step of the design phase requires careful thought and attention to detail, as it ultimately dictates the validity of the entire experiment. From identifying the research question to formulating hypotheses and selecting variables, every aspect of the experiment’s design demands thorough consideration.

Identifying The Research Question

The first step in designing a controlled experiment is identifying the research question. This question serves as the foundation upon which the entire experiment is built. Ensuring that the research question is clear, specific, and measurable is essential to establishing a solid framework for the experiment.

Formulating The Hypothesis

After identifying the research question, the next step involves formulating the hypothesis. The hypothesis should clearly outline the relationship between the variables being studied and is vital in guiding the direction of the experiment.

Selecting The Variables

Upon formulating the hypothesis, selecting the variables to be studied is crucial. This process involves identifying and defining the independent and dependent variables, as well as any extraneous variables that could potentially impact the results.

Developing The Control Group

Another integral component of designing a controlled experiment is developing the control group. The control group serves as the baseline for comparison and allows researchers to isolate the effects of the independent variable on the dependent variable.

Randomization And Sample Size

Randomization and determining the appropriate sample size are critical aspects of experimental design. Random assignment helps minimize the influence of confounding variables, while a sufficient sample size ensures that the results are representative of the population being studied.

Conducting A Controlled Experiment

Define clear objectives and hypotheses for the experiment.
Create a detailed experimental plan with specific steps.
Apply the treatment to the experimental group as planned.
Ensure the control group receives no treatment for comparison.
Use reliable tools and methods to collect data accurately.
Record all data points meticulously for analysis.
Regularly check and adjust factors that could impact the experiment.
Keep conditions consistent across all groups throughout the experiment.
Document all observations in a structured format for analysis.
Ensure all researchers adhere to the observation recording procedure.

Analyzing And Interpreting Results

This study examines the controlled experiment on analyzing and interpreting results, providing valuable insights into the research process to drive decision-making. Discover how data analysis and interpretation play a crucial role in drawing meaningful conclusions and optimizing outcomes.

Data Analysis Techniques

In a controlled experiment, data analysis techniques play a crucial role.

Identifying Trends And Patterns

Identifying trends and patterns helps uncover valuable insights.

Drawing Conclusions

Drawing conclusions from the data leads to actionable outcomes.

Evaluating The Validity And Reliability

Ensuring the validity and reliability of results is essential.

Validation reinforces the credibility of the experiment.

Reliability ensures consistent and trustworthy outcomes.

Thorough analysis aids in assessing the experiment’s success.

Credit: www.khanacademy.org

Limitations And Considerations

In any controlled experiment, it is crucial to consider the limitations and various factors that may impact the study’s outcomes. Understanding potential biases, ethical considerations, generalizability and external validity, limitations of control groups, and addressing confounding variables are essential in ensuring the reliability and validity of the results.

Potential Biases

Potential biases, such as selection bias, measurement bias, or observer bias, can significantly affect the results of a controlled experiment. It’s important to identify and mitigate these biases to ensure the accuracy of the findings.

Ethical Considerations

Ethical considerations play a vital role in the planning and execution of a controlled experiment. It’s essential to uphold ethical standards, including informed consent, safeguarding participants’ privacy, and minimizing any potential harm or distress.

Generalizability And External Validity

Generalizability and external validity refer to the extent to which the findings of a controlled experiment can be applied to a broader population or real-world settings. It’s important to consider these factors to determine the practical implications of the study.

Limitations Of Control Groups

The limitations of control groups, such as ensuring they accurately represent the population being studied and minimizing the impact of variables, need to be carefully addressed to enhance the credibility of the experiment.

Addressing Confounding Variables

Identifying and addressing confounding variables is crucial in controlling for extraneous factors that could influence the outcomes of the experiment. Proper techniques, such as randomization and statistical controls, should be employed to minimize the impact of confounding variables.

Credit: explorebiology.org

Frequently Asked Questions Of Controlled Experiment

What is the meaning of controlled experiment.

A controlled experiment is a research method where variables are carefully controlled to measure the effects of one variable on another. It allows researchers to establish cause and effect relationships by eliminating confounding factors.

What Is An Example Of A Controlled Study?

An example of a controlled study is a clinical trial where participants are assigned to different groups, one receiving the treatment and the other a placebo. This helps to measure the effectiveness of the treatment while controlling for other variables.

What Is The Difference Between Controlled And Uncontrolled Experiments?

Controlled experiments involve manipulating variables, while uncontrolled experiments do not. Controlled experiments offer more reliable results due to the controlled conditions.

What Is A Controlled Cause To Effect Experiment?

In a controlled cause to effect experiment, variables are carefully manipulated to observe specific outcomes.

What Is A Controlled Experiment?

A controlled experiment is a scientific study where variables are carefully controlled to determine cause and effect.

To sum up, conducting controlled experiments is crucial for obtaining accurate and reliable results. By carefully controlling variables, researchers can better understand cause-and-effect relationships. This method enhances the credibility and applicability of scientific findings. As a result, controlled experiments play a vital role in advancing knowledge and innovation across various fields.

Decatenation

Decatenation is the process of removing the links between DNA molecules. It is an important process in DNA replication and repair. Decatenation is a crucial process in DNA replication and repair, involving the separation of linked DNA molecules. During this process, the links between DNA strands are untangled and removed, ensuring the integrity and functionality…

Electron Transport Chain

The Electron Transport Chain (ETC) is a series of protein complexes in the inner mitochondrial membrane that transfer electrons and create a proton gradient. It is a crucial process in cellular respiration, producing the majority of ATP. The ETC plays a vital role in generating energy for the cell, allowing various metabolic processes to occur….

Fimbria syndrome, also known as Fimbriae deficiency, is a rare condition that affects female fertility due to the absence or underdevelopment of the fimbriae, which are finger-like projections at the end of the fallopian tubes. This condition can lead to difficulty in the capture and transport of the egg from the ovary to the uterus,…

Endoparasite

An endoparasite is a parasite that lives inside the body of its host. These parasites can cause various health issues in the host and may require medical treatment. Endoparasites are a diverse group of organisms that can infect humans, animals, and even plants. From microscopic single-celled organisms to larger worms, endoparasites come in various forms…

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Knowledge Base
Methodology
Controlled Experiments | Methods & Examples of Control

Controlled Experiments | Methods & Examples of Control

Published on 19 April 2022 by Pritha Bhandari . Revised on 10 October 2022.

In experiments , researchers manipulate independent variables to test their effects on dependent variables. In a controlled experiment , all variables other than the independent variable are controlled or held constant so they don’t influence the dependent variable.

Controlling variables can involve:

Holding variables at a constant or restricted level (e.g., keeping room temperature fixed)
Measuring variables to statistically control for them in your analyses
Balancing variables across your experiment through randomisation (e.g., using a random order of tasks)

Why does control matter in experiments, methods of control, problems with controlled experiments, frequently asked questions about controlled experiments.

Control in experiments is critical for internal validity , which allows you to establish a cause-and-effect relationship between variables.

Your independent variable is the colour used in advertising.
Your dependent variable is the price that participants are willing to pay for a standard fast food meal.

Extraneous variables are factors that you’re not interested in studying, but that can still influence the dependent variable. For strong internal validity, you need to remove their effects from your experiment.

Design and description of the meal
Study environment (e.g., temperature or lighting)
Participant’s frequency of buying fast food
Participant’s familiarity with the specific fast food brand
Participant’s socioeconomic status

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You can control some variables by standardising your data collection procedures. All participants should be tested in the same environment with identical materials. Only the independent variable (e.g., advert colour) should be systematically changed between groups.

Other extraneous variables can be controlled through your sampling procedures . Ideally, you’ll select a sample that’s representative of your target population by using relevant inclusion and exclusion criteria (e.g., including participants from a specific income bracket, and not including participants with colour blindness).

By measuring extraneous participant variables (e.g., age or gender) that may affect your experimental results, you can also include them in later analyses.

After gathering your participants, you’ll need to place them into groups to test different independent variable treatments. The types of groups and method of assigning participants to groups will help you implement control in your experiment.

Control groups

Controlled experiments require control groups . Control groups allow you to test a comparable treatment, no treatment, or a fake treatment, and compare the outcome with your experimental treatment.

You can assess whether it’s your treatment specifically that caused the outcomes, or whether time or any other treatment might have resulted in the same effects.

A control group that’s presented with red advertisements for a fast food meal
An experimental group that’s presented with green advertisements for the same fast food meal

Random assignment

To avoid systematic differences between the participants in your control and treatment groups, you should use random assignment .

This helps ensure that any extraneous participant variables are evenly distributed, allowing for a valid comparison between groups .

Random assignment is a hallmark of a ‘true experiment’ – it differentiates true experiments from quasi-experiments .

Masking (blinding)

Masking in experiments means hiding condition assignment from participants or researchers – or, in a double-blind study , from both. It’s often used in clinical studies that test new treatments or drugs.

Sometimes, researchers may unintentionally encourage participants to behave in ways that support their hypotheses. In other cases, cues in the study environment may signal the goal of the experiment to participants and influence their responses.

Using masking means that participants don’t know whether they’re in the control group or the experimental group. This helps you control biases from participants or researchers that could influence your study results.

Although controlled experiments are the strongest way to test causal relationships, they also involve some challenges.

Difficult to control all variables

Especially in research with human participants, it’s impossible to hold all extraneous variables constant, because every individual has different experiences that may influence their perception, attitudes, or behaviors.

But measuring or restricting extraneous variables allows you to limit their influence or statistically control for them in your study.

Risk of low external validity

Controlled experiments have disadvantages when it comes to external validity – the extent to which your results can be generalised to broad populations and settings.

The more controlled your experiment is, the less it resembles real world contexts. That makes it harder to apply your findings outside of a controlled setting.

There’s always a tradeoff between internal and external validity . It’s important to consider your research aims when deciding whether to prioritise control or generalisability in your experiment.

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

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Experiment Definition in Science – What Is a Science Experiment?

In science, an experiment is simply a test of a hypothesis in the scientific method . It is a controlled examination of cause and effect. Here is a look at what a science experiment is (and is not), the key factors in an experiment, examples, and types of experiments.

Experiment Definition in Science

By definition, an experiment is a procedure that tests a hypothesis. A hypothesis, in turn, is a prediction of cause and effect or the predicted outcome of changing one factor of a situation. Both the hypothesis and experiment are components of the scientific method. The steps of the scientific method are:

Make observations.
Ask a question or identify a problem.
State a hypothesis.
Perform an experiment that tests the hypothesis.
Based on the results of the experiment, either accept or reject the hypothesis.
Draw conclusions and report the outcome of the experiment.

Key Parts of an Experiment

The two key parts of an experiment are the independent and dependent variables. The independent variable is the one factor that you control or change in an experiment. The dependent variable is the factor that you measure that responds to the independent variable. An experiment often includes other types of variables , but at its heart, it’s all about the relationship between the independent and dependent variable.

Examples of Experiments

Fertilizer and plant size.

For example, you think a certain fertilizer helps plants grow better. You’ve watched your plants grow and they seem to do better when they have the fertilizer compared to when they don’t. But, observations are only the beginning of science. So, you state a hypothesis: Adding fertilizer increases plant size. Note, you could have stated the hypothesis in different ways. Maybe you think the fertilizer increases plant mass or fruit production, for example. However you state the hypothesis, it includes both the independent and dependent variables. In this case, the independent variable is the presence or absence of fertilizer. The dependent variable is the response to the independent variable, which is the size of the plants.

Now that you have a hypothesis, the next step is designing an experiment that tests it. Experimental design is very important because the way you conduct an experiment influences its outcome. For example, if you use too small of an amount of fertilizer you may see no effect from the treatment. Or, if you dump an entire container of fertilizer on a plant you could kill it! So, recording the steps of the experiment help you judge the outcome of the experiment and aid others who come after you and examine your work. Other factors that might influence your results might include the species of plant and duration of the treatment. Record any conditions that might affect the outcome. Ideally, you want the only difference between your two groups of plants to be whether or not they receive fertilizer. Then, measure the height of the plants and see if there is a difference between the two groups.

Salt and Cookies

You don’t need a lab for an experiment. For example, consider a baking experiment. Let’s say you like the flavor of salt in your cookies, but you’re pretty sure the batch you made using extra salt fell a bit flat. If you double the amount of salt in a recipe, will it affect their size? Here, the independent variable is the amount of salt in the recipe and the dependent variable is cookie size.

Test this hypothesis with an experiment. Bake cookies using the normal recipe (your control group ) and bake some using twice the salt (the experimental group). Make sure it’s the exact same recipe. Bake the cookies at the same temperature and for the same time. Only change the amount of salt in the recipe. Then measure the height or diameter of the cookies and decide whether to accept or reject the hypothesis.

Examples of Things That Are Not Experiments

Based on the examples of experiments, you should see what is not an experiment:

Making observations does not constitute an experiment. Initial observations often lead to an experiment, but are not a substitute for one.
Making a model is not an experiment.
Neither is making a poster.
Just trying something to see what happens is not an experiment. You need a hypothesis or prediction about the outcome.
Changing a lot of things at once isn’t an experiment. You only have one independent and one dependent variable. However, in an experiment, you might suspect the independent variable has an effect on a separate. So, you design a new experiment to test this.

Types of Experiments

There are three main types of experiments: controlled experiments, natural experiments, and field experiments,

Controlled experiment : A controlled experiment compares two groups of samples that differ only in independent variable. For example, a drug trial compares the effect of a group taking a placebo (control group) against those getting the drug (the treatment group). Experiments in a lab or home generally are controlled experiments
Natural experiment : Another name for a natural experiment is a quasi-experiment. In this type of experiment, the researcher does not directly control the independent variable, plus there may be other variables at play. Here, the goal is establishing a correlation between the independent and dependent variable. For example, in the formation of new elements a scientist hypothesizes that a certain collision between particles creates a new atom. But, other outcomes may be possible. Or, perhaps only decay products are observed that indicate the element, and not the new atom itself. Many fields of science rely on natural experiments, since controlled experiments aren’t always possible.
Field experiment : While a controlled experiments takes place in a lab or other controlled setting, a field experiment occurs in a natural setting. Some phenomena cannot be readily studied in a lab or else the setting exerts an influence that affects the results. So, a field experiment may have higher validity. However, since the setting is not controlled, it is also subject to external factors and potential contamination. For example, if you study whether a certain plumage color affects bird mate selection, a field experiment in a natural environment eliminates the stressors of an artificial environment. Yet, other factors that could be controlled in a lab may influence results. For example, nutrition and health are controlled in a lab, but not in the field.
Bailey, R.A. (2008). Design of Comparative Experiments . Cambridge: Cambridge University Press. ISBN 9780521683579.
di Francia, G. Toraldo (1981). The Investigation of the Physical World . Cambridge University Press. ISBN 0-521-29925-X.
Hinkelmann, Klaus; Kempthorne, Oscar (2008). Design and Analysis of Experiments. Volume I: Introduction to Experimental Design (2nd ed.). Wiley. ISBN 978-0-471-72756-9.
Holland, Paul W. (December 1986). “Statistics and Causal Inference”. Journal of the American Statistical Association . 81 (396): 945–960. doi: 10.2307/2289064
Stohr-Hunt, Patricia (1996). “An Analysis of Frequency of Hands-on Experience and Science Achievement”. Journal of Research in Science Teaching . 33 (1): 101–109. doi: 10.1002/(SICI)1098-2736(199601)33:1<101::AID-TEA6>3.0.CO;2-Z

What Is A Controlled Experiment? Aren’t All Experiments Controlled?

Why should you experiment, how should you experiment, key parameters of a controlled experiment, is there such a thing as an uncontrolled experiment.

A procedure that helps you understand the influence of various factors that affect a result and the extent of their effect in a controlled environment.

Have you ever done science experiments that have numerous parameters you need to take care of to get an accurate result?

If so, I know exactly how that feels!

Most of the time, you won’t get a perfect value, but rather a value that is nearly correct. It can be so frustrating at times, as you need to take care of the amount of catalyst, the temperature, pressure and a million other things!

I wonder who found out that you need precisely ‘this’ thing in exactly ‘this’ amount to get ‘that’ thing! Well, over time, I’ve realized just how much important these parameters are. These values help us set up a controlled environment where the experiment can occur.

And while many people loathe doing lengthy experiments, scientists have performed these exact same experiments a million times to find the perfect mix of parameters that give a predictable result! Now that’s perseverance!!

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Why Are Rats The Most Preferred Animals For Experiments?

Happy,Beautiful,Twins,Girls,Point,Up,Isolated,On,Blue,Background,

Are We Genetically Predetermined To Like What We Like?

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Endocrine System: Glands and Hormones

Germination: How Does A Seed Become A Plant?

Do Plants Next To Roads ABSORB Pollution And Become HARMFUL?

Photosynthesis: The Biochemistry Behind How Plants Make Their Food

Max Pain: How Much Pain Can A Person Take?

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Knowledge Base

Methodology

Control Groups and Treatment Groups | Uses & Examples

Control Groups and Treatment Groups | Uses & Examples

Published on July 3, 2020 by Lauren Thomas . Revised on June 22, 2023.

In a scientific study, a control group is used to establish causality by isolating the effect of an independent variable .

Here, researchers change the independent variable in the treatment group and keep it constant in the control group. Then they compare the results of these groups.

Using a control group means that any change in the dependent variable can be attributed to the independent variable. This helps avoid extraneous variables or confounding variables from impacting your work, as well as a few types of research bias , like omitted variable bias .

Control groups in experiments, control groups in non-experimental research, importance of control groups, other interesting articles, frequently asked questions about control groups.

Control groups are essential to experimental design . When researchers are interested in the impact of a new treatment, they randomly divide their study participants into at least two groups:

The treatment group (also called the experimental group ) receives the treatment whose effect the researcher is interested in.
The control group receives either no treatment, a standard treatment whose effect is already known, or a placebo (a fake treatment to control for placebo effect ).

The treatment is any independent variable manipulated by the experimenters, and its exact form depends on the type of research being performed. In a medical trial, it might be a new drug or therapy. In public policy studies, it could be a new social policy that some receive and not others.

In a well-designed experiment, all variables apart from the treatment should be kept constant between the two groups. This means researchers can correctly measure the entire effect of the treatment without interference from confounding variables .

You pay the students in the treatment group for achieving high grades.
Students in the control group do not receive any money.

Studies can also include more than one treatment or control group. Researchers might want to examine the impact of multiple treatments at once, or compare a new treatment to several alternatives currently available.

The treatment group gets the new pill.
Control group 1 gets an identical-looking sugar pill (a placebo)
Control group 2 gets a pill already approved to treat high blood pressure

Since the only variable that differs between the three groups is the type of pill, any differences in average blood pressure between the three groups can be credited to the type of pill they received.

The difference between the treatment group and control group 1 demonstrates the effectiveness of the pill as compared to no treatment.
The difference between the treatment group and control group 2 shows whether the new pill improves on treatments already available on the market.

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Although control groups are more common in experimental research, they can be used in other types of research too. Researchers generally rely on non-experimental control groups in two cases: quasi-experimental or matching design.

Control groups in quasi-experimental design

While true experiments rely on random assignment to the treatment or control groups, quasi-experimental design uses some criterion other than randomization to assign people.

Often, these assignments are not controlled by researchers, but are pre-existing groups that have received different treatments. For example, researchers could study the effects of a new teaching method that was applied in some classes in a school but not others, or study the impact of a new policy that is implemented in one state but not in the neighboring state.

In these cases, the classes that did not use the new teaching method, or the state that did not implement the new policy, is the control group.

Control groups in matching design

In correlational research , matching represents a potential alternate option when you cannot use either true or quasi-experimental designs.

In matching designs, the researcher matches individuals who received the “treatment”, or independent variable under study, to others who did not–the control group.

Each member of the treatment group thus has a counterpart in the control group identical in every way possible outside of the treatment. This ensures that the treatment is the only source of potential differences in outcomes between the two groups.

Control groups help ensure the internal validity of your research. You might see a difference over time in your dependent variable in your treatment group. However, without a control group, it is difficult to know whether the change has arisen from the treatment. It is possible that the change is due to some other variables.

If you use a control group that is identical in every other way to the treatment group, you know that the treatment–the only difference between the two groups–must be what has caused the change.

For example, people often recover from illnesses or injuries over time regardless of whether they’ve received effective treatment or not. Thus, without a control group, it’s difficult to determine whether improvements in medical conditions come from a treatment or just the natural progression of time.

Risks from invalid control groups

If your control group differs from the treatment group in ways that you haven’t accounted for, your results may reflect the interference of confounding variables instead of your independent variable.

Minimizing this risk

A few methods can aid you in minimizing the risk from invalid control groups.

Ensure that all potential confounding variables are accounted for , preferably through an experimental design if possible, since it is difficult to control for all the possible confounders outside of an experimental environment.
Use double-blinding . This will prevent the members of each group from modifying their behavior based on whether they were placed in the treatment or control group, which could then lead to biased outcomes.
Randomly assign your subjects into control and treatment groups. This method will allow you to not only minimize the differences between the two groups on confounding variables that you can directly observe, but also those you cannot.

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
Quartiles & Quantiles
Cluster sampling
Stratified sampling
Data cleansing
Reproducibility vs Replicability
Peer review
Prospective cohort study

Research bias

Implicit bias
Cognitive bias
Placebo effect
Hawthorne effect
Hindsight bias
Affect heuristic
Social desirability bias

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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.

A true experiment (a.k.a. a controlled experiment) always includes at least one control group that doesn’t receive the experimental treatment.

However, some experiments use a within-subjects design to test treatments without a control group. In these designs, you usually compare one group’s outcomes before and after a treatment (instead of comparing outcomes between different groups).

For strong internal validity , it’s usually best to include a control group if possible. Without a control group, it’s harder to be certain that the outcome was caused by the experimental treatment and not by other variables.

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.

There are several methods you can use to decrease the impact of confounding variables on your research: restriction, matching, statistical control and randomization.

In restriction , you restrict your sample by only including certain subjects that have the same values of potential confounding variables.

In matching , you match each of the subjects in your treatment group with a counterpart in the comparison group. The matched subjects have the same values on any potential confounding variables, and only differ in the independent variable .

In statistical control , you include potential confounders as variables in your regression .

In randomization , you randomly assign the treatment (or independent variable) in your study to a sufficiently large number of subjects, which allows you to control for all potential confounding variables.

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.

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A controlled experiment is one in which everything is held constant except for one variable . Usually, a set of data is taken to be a control group , which is commonly the normal or usual state, and one or more other groups are examined where all conditions are identical to the control group and to each other except for one variable.

Sometimes it's necessary to change more than one variable, but all of the other experimental conditions will be controlled so that only the variables being examined change. And what is measured is the variables' amount or the way in which they change.

Controlled Experiment

A controlled experiment is simply an experiment in which all factors are held constant except for one: the independent variable.
A common type of controlled experiment compares a control group against an experimental group. All variables are identical between the two groups except for the factor being tested.
The advantage of a controlled experiment is that it is easier to eliminate uncertainty about the significance of the results.

Example of a Controlled Experiment

Let's say you want to know if the type of soil affects how long it takes a seed to germinate, and you decide to set up a controlled experiment to answer the question. You might take five identical pots, fill each with a different type of soil, plant identical bean seeds in each pot, place the pots in a sunny window, water them equally, and measure how long it takes for the seeds in each pot to sprout.

This is a controlled experiment because your goal is to keep every variable constant except the type of soil you use. You control these features.

Why Controlled Experiments Are Important

The big advantage of a controlled experiment is that you can eliminate much of the uncertainty about your results. If you couldn't control each variable, you might end up with a confusing outcome.

For example, if you planted different types of seeds in each of the pots, trying to determine if soil type affected germination, you might find some types of seeds germinate faster than others. You wouldn't be able to say, with any degree of certainty, that the rate of germination was due to the type of soil. It might as well have been due to the type of seeds.

Or, if you had placed some pots in a sunny window and some in the shade or watered some pots more than others, you could get mixed results. The value of a controlled experiment is that it yields a high degree of confidence in the outcome. You know which variable caused or did not cause a change.

Are All Experiments Controlled?

No, they are not. It's still possible to obtain useful data from uncontrolled experiments, but it's harder to draw conclusions based on the data.

An example of an area where controlled experiments are difficult is human testing. Say you want to know if a new diet pill helps with weight loss. You can collect a sample of people, give each of them the pill, and measure their weight. You can try to control as many variables as possible, such as how much exercise they get or how many calories they eat.

However, you will have several uncontrolled variables, which may include age, gender, genetic predisposition toward a high or low metabolism, how overweight they were before starting the test, whether they inadvertently eat something that interacts with the drug, etc.

Scientists try to record as much data as possible when conducting uncontrolled experiments, so they can see additional factors that may be affecting their results. Although it is harder to draw conclusions from uncontrolled experiments, new patterns often emerge that would not have been observable in a controlled experiment.

For example, you may notice the diet drug seems to work for female subjects, but not for male subjects, and this may lead to further experimentation and a possible breakthrough. If you had only been able to perform a controlled experiment, perhaps on male clones alone, you would have missed this connection.

Box, George E. P., et al. Statistics for Experimenters: Design, Innovation, and Discovery . Wiley-Interscience, a John Wiley & Soncs, Inc., Publication, 2005.
Creswell, John W. Educational Research: Planning, Conducting, and Evaluating Quantitative and Qualitative Research . Pearson/Merrill Prentice Hall, 2008.
Pronzato, L. "Optimal experimental design and some related control problems". Automatica . 2008.
Robbins, H. "Some Aspects of the Sequential Design of Experiments". Bulletin of the American Mathematical Society . 1952.
Understanding Simple vs Controlled Experiments
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What Is a Hypothesis? (Science)
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Chapter 1 Study Questions

A Choose matching term 1 In presenting data that result from an experiment, a group of students show that most of their measurements fall on a straight diagonal line on their graph. However, two of their data points are "outliers" and fall far to one side of the expected relationship. What should they do? A - Do not show these points but write a footnote that the graph represents the correct data. B - Change the details of the experiment until they can obtain the expected results. C - Show all results obtained and then try to explore the reason(s) for these outliers. D - Throw out this set of data and try again. E - Average several trials and therefore rule out the improbable results. 2 Which of the following is an example of qualitative data? A - The six pairs of robins hatched an average of three chicks. B - The plant's height is 25 centimeters (cm). C - The temperature decreased from 20°C to 15°C. D - The contents of the stomach are mixed every 20 seconds. E - The fish swam in a zigzag motion. 3 A controlled experiment is one in which A - there are at least two groups, one of which does not receive the experimental treatment. B - there is one group for which the scientist controls all variables. C - there are at least two groups, one differing from the other by two or more variables. D - the experiment proceeds at a slow pace to guarantee that the scientist can carefully observe all reactions and process all experimental data. E - the experiment is repeated many times to ensure that the results are accurate. 4 Imagine there is a species-specific fishing regulation that mandates only adult fish of this species that are 75 cm or longer may be kept and shorter fish must be released. Based on your knowledge of natural selection, you would predict that the average length of the adult fish population will A - gradually decline. B - remain unchanged. C - rapidly decline. D - rapidly increase. E - gradually increase. Don't know?

Assessing Scientific Inquiry: A Systematic Literature Review of Tasks, Tools and Techniques

Theoretical Studies
Open access
Published: 04 September 2024

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De Van Vo ORCID: orcid.org/0000-0002-8515-0221 1 &
Geraldine Mooney Simmie ORCID: orcid.org/0000-0002-5026-4261 1

While national curricula in science education highlight the importance of inquiry-based learning, assessing students’ capabilities in scientific inquiry remains a subject of debate. Our study explored the construction, developmental trends and validation techniques in relation to assessing scientific inquiry using a systematic literature review from 2000 to 2024. We used PRISMA guidelines in combination with bibliometric and Epistemic Network Analyses. Sixty-three studies were selected, across all education sectors and with a majority of studies in secondary education. Results showed that assessing scientific inquiry has been considered around the world, with a growing number (37.0%) involving global researcher networks focusing on novel modelling approaches and simulation performance in digital-based environments. Although there was modest variation between the frameworks, studies were mainly concerned with cognitive processes and psychological characteristics and were reified from wider ethical, affective, intersectional and socio-cultural considerations. Four core categories (formulating questions/hypotheses, designing experiments, analysing data, and drawing conclusions) were most often used with nine specific components (formulate questions formulate prediction/hypotheses, set experiment, vary independent variable, measure dependent variable, control confounding variables, describe data, interpret data, reach reasonable conclusion). There was evidence of transitioning from traditional to online modes, facilitated by interactive simulations, but the independent tests and performance assessments, in both multiple-choice and open-ended formats remained the most frequently used approach with a greater emphasis on context than heretofore. The findings will be especially useful for science teachers, researchers and policy decision makers with an active interest in assessing capabilities in scientific inquiry.

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Introduction

In contemporary times as more information and knowledge are created in a shorter timeline, the need for scientific literacy and inquiry-based capabilities beyond nature of science is increasing, especially in relation to the pressing needs of the wider world (Erduran, 2014 ). This is a growing concern, in relation to the future survival of humanity and sustainability of the planet for the reconceptualization of science education for epistemic justice and the foregrounding of intersectionality (Wallace et al., 2022 ). At the same time, policymakers and employers demand 21st century skills and inquiry-oriented approaches that include creativity, critical thinking, collaboration, communication and digital competencies (Binkley et al., 2012 ; Chu et al., 2017 ; Voogt & Roblin, 2012 ). Rather than teaching extensive content knowledge, there is a policy imperative to teach skills, dispositions, literacies and inquiry-oriented competencies. Mastery of capabilities, such as inquiry-oriented learning has therefore become a core outcome of national science education curricula globally (Baur et al., 2022 ).

Inquiry orientations are continuously emphasized in science education by the Organisation for Economic Cooperation and Development (OECD) operating in more than forty countries globally (OECD, 2015 , 2017 ) in the US (National Research Council [NRC], 2000 ), in Europe (European Commission and Directorate-General for Research and Innovation, 2015 ), and in nation states, such as in Ireland with the National Council for Curriculum and Assessment (NCCA, 2015 ).

The policy imperative for inquiry-oriented activities in science classrooms prompts a growing interest in assessing students’ scientific inquiry capabilities. While scientific inquiry is a well-established research area in science education (Fukuda et al., 2022 ), assessing students’ scientific inquiry capabilities is a growing topic of research, innovation and consideration.

There is a growing demand for innovative assessments that aim to either enhance or replace traditional summative methods. These assessments should focus on creating customized, student-centered formative tasks, tools, and techniques that capture both the final products and the processes used to achieve them (Hattie & Timperley, 2007 ). Many researchers argue that traditional models, originally designed to measure content knowledge, are no longer adequate for assessing competencies. Griffin et al. ( 2012 ) argued that traditional methods lack the ability to measure the higher-order skills, dispositions, and knowledge requirements of collaborative learning. Instead, it is asserted that modes of formative assessment can provide teachers and students with diagnostic information in order to continually adapt instruction and to foster a pedagogical cycle of learning (Kruit et al., 2018 ; Voogt & Roblin, 2012 ).

In this study, we systematically examined the construction, developmental trends and validation tasks, tools and techniques used in assessing students’ scientific inquiry capabilities in educational settings. We combined a systematic literature review from 2000 to 2024, using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines with Bibliometric (Diodato & Gellatly, 2013 ) and Epistemic Network Analyses (ENA) (Shaffer et al., 2016 ). Our aim was to illuminate current global trends, possibilities and challenges in relation to the assessment of scientific inquiry and to suggest potential spaces for future research. Our study was guided by the following three research questions:

RQ1: To what extent is research on assessment of scientific inquiry in educational contexts found in the international literature?

RQ2: What are the predominant components, tasks, tools, and techniques used to assess scientific inquiry?

RQ3: What are the trends and developments in the assessment of scientific inquiry?

We structured the paper as follows. First, we briefly interrogate current conceptualisations of inquiry-based learning and scientific inquiry as an important background to the study. Second, we justify our selected methodology, the use of a systematic literature review with bibliometric and ENA analyses. Third, we present the results from each research question in turn. Finally, we discuss the changing shape of this research domain and the implications for the future of science education.

Conceptualizations of Scientific Inquiry

Here we first explore the construct of inquiry-based learning in science education before considering something of the global policy imperatives underway in this regard.

Inquiry-based Approach in Science Education

In science education, two visions of scientific literacy are discussed: Vision I emphasizes scientific content and propositional knowledge, while Vision II focuses on engaging students with real-world applications of science knowledge (Roberts, 2007 ; Roberts & Bybee, 2014 ). Achieving the scientific literacy mentioned in Vision II literacy is a key challenge for 21st-century science education, shifting towards enabling individuals to apply scientific concepts in everyday life rather than solely producing ‘mini-scientists’ (Roberts & Bybee, 2014 ). Balancing these visions is crucial to meeting diverse student needs and enhancing understanding science-in-context in today’s highly scientific world (Roberts & Bybee, 2014 ). Scientific inquiry is considered fundamental to scientific literacy, encompassing practices and epistemology, with a growing focus on the meaning, application and contexts of real world inquiry (Schwartz et al., 2023 ).

An inquiry-orientation therefore provides a pedagogical approach in which students learn by actively using scientific methods to reason and generate explanations in relation to design, data and evidence (Anderson, 2002 ; Stender et al., 2018 ). Neumann et al. ( 2011 ) considered the Nature of Science and Scientific Inquiry as separate domains for inquiry-orientations including for analysing data, identifying and controlling variables, and forming logical cause-and‐effect relationships. Wenning ( 2007 ) proposed a detailed rubric for developing proficiency in scientific inquiry, that included identifying a problem to be investigated, doing background research, using induction, formulating a hypothesis, incorporating logic and evidence, using deduction, generating a prediction, designing experimental procedures to test the prediction, conducting a scientific experiment, observing or simulating a test or model, collecting data, organizing, and analysing data accurately and precisely, applying statistical methods to support conclusions and communicating results. Moreover, Turner et al. ( 2018 ) grouped sixteen of the activities into three components of inquiry for secondary school students in science and math classrooms, namely working with hypotheses (i.e., generation of hypotheses/predictions, designing procedures), communication in inquiry (i.e., interpreting outcomes, asking questions), hands-on inquiry (i.e., recording data, visualising data).

Pedaste et al. ( 2015 ) conceptualised an inquiry-based learning framework of four phases based on their review of thirty-two studies: orientation , conceptualization , investigation , and conclusion . The orientation phase stimulates interest and curiosity, involves background research and results in the writing of a problem statement or topic by the teacher and/or students. Conceptualization involves formulating theory-based questions as predictions or hypotheses. The investigation phase turns curiosity into action through exploration, experimentation, data gathering and interpretation. In the conclusion phase, learners address their original research questions and consider whether these questions are answered, supported or refuted.

The studies showed that the inquiry-orientation enhanced comprehension (Marshall et al., 2017 ), fostered an appreciation of the nature of scientific knowledge (Dogan et al., 2024 ), improved students’ achievement in both scientific practices and conceptual knowledge (Marshall et al., 2017 ). Inquiry-based approach was found to positively impact student engagement and motivation while the hands-on experimental skills made learning science more enjoyable (Ramnarain, 2014 ). Inquiry activities make learning visible and help to integrate scientific reasoning skills for the social construction of knowledge (Stender et al., 2018 ).

Global Policy Imperatives in Relation to Scientific Inquiry

The US National Science Education Standards presented by the National Research Council (NRC, 1996 ) defined inquiry is “a multifaceted activity that involves making observations; posing questions; examining books and other sources of information to see what is already known; planning investigations; reviewing what is already known in light of experimental evidence; using tools to gather, analyze, and interpret data; proposing answers, explanations, and predictions; and communicating the results” (p. 23). Scientific inquiry encompasses the various methods scientists use to investigate the natural world and formulate explanations grounded in evidence from their research. It also involves students’ activities where they gain knowledge and understanding of scientific concepts and learn about the processes which scientists use to explore the natural world.

Later NRC standards (2000, 2006) elaborated such proficiency as identifying a scientific question, designing and conducting an investigation, using appropriate tools to collect and analyse data, and developing evidence-based explanations. The US framework for K-12 science education (NRC, 2012 ) focused on a few core ideas and concepts, integrating them with the practices needed for scientific inquiry and engineering design. The emphasis appeared to have shifted from “inquiry” to “scientific practices” as a basis of the framework (Rönnebeck et al., 2016 ). It listed eight components of scientific and engineering practices, including asking questions, developing and using models, planning and carrying out investigations, analyzing and interpreting data, using mathematics and computational thinking, constructing explanations, engaging in argument from evidence, obtaining, evaluating, and communicating information (NRC, 2012 ). The eight practices intentionally intersect and connect with others rather than stand-alone (NRC, 2012 ; Rönnebeck et al., 2016 ).

The Twenty First Century Science program (2006) in England emphasized a broad qualitative understanding of significant “whole explanations” and placed a strong focus on Ideas about Science . It also prioritized developing the understanding and skills needed to critically evaluate scientific information encountered in everyday life. This initiative focuses on a foundational course aimed at fostering scientific literacy among all students. It emphasized equipping students with the knowledge and skills needed to critically evaluate scientific information encountered in daily life. This connects science to real-world contexts and applications, and the big ideas of science rather than isolated facts (Millar, 2006 ).

The 2015 Programme for International Student Assessment (PISA) specified a number of essential science inquiry competencies in three key areas: explaining phenomena scientifically, interpreting data and evidence scientifically, and evaluating and designing scientific inquiry (OECD, 2017 ). The explaining phenomena dimension involves students being able to identify, provide, and assess explanations for a variety of natural and technological phenomena. The interpreting dimension means that students can describe and evaluate scientific investigations and suggest methods for scientifically addressing questions. The designing dimension refers to students who can analyse and assess claims and arguments presented in various forms and draw accurate scientific conclusions (OECD, 2017 ).

In the 21st-century vision for science education in Europe, involving citizens as active participants in inquiry-oriented learning was essential (European Commission and Directorate-General for Research and Innovation, 2015 ). The scientific inquiry involves students identifying research problems and finding solutions that apply science to everyday life. Inquiry-based science education engages students in problem-based learning, hands-on experiments, self-regulated learning, and collaborative discussion, fostering a deep understanding of science and awareness of the practical applications of scientific concepts.

In summary, global policy imperatives focus on enhancing the cognitive processes and psychological characteristics of scientific inquiry and its application in real-world contexts. This approach consistently emphasizes inquiry as fundamental to teaching and learning science, although the focus has varied over time between Vision I and Vision II in relation to scientific literacy and science education.

Methodology

For the systematic literature review, we used the PRISMA methodology (Moher et al., 2009 ) in order to assemble an evidence base of relevant studies. This was further supported by Bibliometric analysis (Diodato & Gellatly, 2013 ) and ENA analysis (Shaffer et al., 2016 ). Bibliometric analysis is a quantitative method used to evaluate various aspects of academic publications within a specified field of study. It involves the application of mathematical and statistical tools to analyse patterns, and impact within a defined body of literature. It is a powerful tool for analysing the knowledge framework and structure in a specific research area (Diodato & Gellatly, 2013 ). Meanwhile, ENA is an analytical method to describe individual (group) framework patterns through quantitative analysis of data by examining the structure of the co-occurrence or connections in coded data (Shaffer et al., 2016 ). ENA can be used to compare units of analysis in terms of their plotted point positions, individual networks, mean plotted point positions, and mean networks, which average the connection weights across individual networks. This approach has been applied in several fields, including educational research (Ruis & Lee, 2021 ).

A comprehensive examination of extant literature was undertaken using the PRISMA-framework stages, with a specific focus on empirical research. The criterion for article selection was predicated on the utilization of a testing instrument for assessment of scientific inquiry. The inclusion criteria were threefold. Firstly, empirical studies that assessed the information retrieval abilities of students - qualitative, quantitative, or mixed methods - were considered. Secondly, the selected studies were required to incorporate scientific inquiry assessment tasks for K-12 science education. Thirdly, the chosen articles were limited to those originally published in the English language and within a timeline from 2000 to 2024 (09/06/2024).

We conducted a systematic search for academic papers in electronic databases as presented in Fig. 1 , employing specific search terms in the title, keywords, and abstract sections: (“inquiry” OR “scientific inquiry” OR “science inquiry” OR “investigation skill”) AND (“assessment” OR “testing” OR “measurement” OR “computer-based assessment”) AND NOT (“review”). The review used two scientific databases: Scopus and Web of Science (WoS). The results in Scopus and WoS suggested 2228 and 1532 references respectively through the first search strategy. After merging the two datasets based on articles’ DOIs indices, as well as following the removal of duplicate entries, we reached 589 articles. We continued to check the titles and abstracts of the remaining articles for pre-selection purposes based on our predefined inclusion criteria. The process led to the identification of 263 papers for further consideration. In this stage, the authors further discussed and agreed on the inclusion criteria, content relevance, methodological quality, and methodological relevance for the selection of papers. We also facilitated discussions among raters to build consensus on ambiguous cases. Finally, we ended up with sixty-three articles selected for our dataset. Then, the data were manually entered one by one, coded and documented for final selection.

Flowchart of the inclusion and exclusion process following PRISMA guidelines

To server our research questions, we collected information from the selected articles as a dataset for thematic analysis in the PRISMA framework. This information included: (1) year of publication, (2) age groups of the participants (categorized into four age groups: 5–10 years, 11–15 ages and 16–18 ages, (3) study context, (4) components of scientific inquiry, (5) instruments/tests, and (6) technique/validation approaches. (Readers can access full raw data at https://osf.io/5bt82 ).

For bibliometric analysis, the data of the selected articles was exported from the Scopus platform. It involved common bibliographical information such authors, title, year, DOI, affiliation, abstract, keyword and reference. We used bibliometric analysis via R software version 4.2.3 (R Core Team, 2023 ) with shiny (Chang et al., 2023 ) and bibliometrix package (Aria & Cuccurullo, 2017 ).

To facilitate for ENA analysis, we coded the data regarding components of scientific inquiry, based on existing frameworks (Table 1 ). The analyses were employed via ENA Web Tool (Marquart et al., 2018 ).

The results are presented here in relation to the key research questions. First, we present surface characteristics that provide a general overview of empirical studies on assessing scientific inquiry worldwide. Then, we explore the components, constructs, and techniques most often used in these assessments across the empirical studies with specific illustrative examples highlighted. Finally, we review the results to identify trends and developments in the assessment of scientific inquiry over time.

Studies on Measuring Scientific Inquiry in School Contexts Worldwide

The 63 selected articles comprised a total of 189 authors, with only four single-author articles (Kind, 2013 ; Mutlu, 2020 ; Sarıoğlu, 2023 ; Teig, 2024 ). Bibliometric analysis showed 3194 references cited, while international co-author index and co-author per article was 17.46% and 3.62, respectively. There were 21 papers published from 2000 to 2012. This number more than doubled to 42 articles from 2013 to 2024. The articles were published in 29 journals, with the core source recognized for the International Journal of Science Education ( IJSE) (11 articles) and the Journal of Research in Science Teaching (JRST) (10 articles), followed by the International Journal of Science and Mathematics Education ( IJSME) (7 articles), and the Research in Science and Technological Education (RSTE) (3 articles). Figure 2 depicts the cumulative articles of the core sources’ production during the period from 2000 to 2024. The graph shows the major journals contributing to this field of study (IJSE, JRST and IJSME), and the noticeable growth curve in the last decade.

The cumulative occurrence of articles in key journals published over time

The findings showed that the 63 articles have a global reach, with study contexts spanning 19 different countries and territories. Notably, a high proportion of studies (23 articles, 36.5%) come from the United States, followed by Taiwan (9 articles, 14.3%), Turkey (5 articles, 7.9%), and Germany (4 articles, 6.3%), while Israel and China each contributed 3 studies (4.8%). The distribution indicates that assessing scientific inquiry is a relatively attractive area of research in science education at a global level.

Regarding affiliation contribution, Fig. 3 shows that five universities emerge as the significant contributors to this collection of publications. Among these institutions, two are located in the US: The University of California (UC) and the Caltech Precollege Science Initiative (CAPSI). UC has remained consistently active in the field since 2002, while CAPSI’s involvement has stagnated since 2005. Humboldt University in Berlin (HU-Berlin) began contributing in 2012. Meanwhile, the National Taiwan Normal University (NTNU) has been actively contributing since 2013, with a sharp increase in activity. Beijing Normal University (BNU) entered the research landscape later, but has shown a steady increase in contributions recently. It is noted that the contributions refer to the frequency distribution of affiliations of all co-authors for each paper (Aria & Cuccurullo, 2017 ).

Top 5 of the research institution contribution over time

With respect to collaboration network in the research field, Fig. 4 represents collaborative patterns among researchers in selected articles, covering author and country levels. Based on the studies selected, the analysis identified 11 distinct research networks, illustrated in Fig. 4 a, that present as networks with a significant number of researchers. For instance, in the networks, we can find research groups such as the ones led by Wu, Linn, and Gobert. Furthermore, Fig. 4 b shows that the United States play a pivotal role in leading out international collaborations within the field of scientific inquiry assessment.

Collaboration networks of researchers identified in the articles selected

The cumulative participant count involved in all the studies totalled 50,470 individuals, encompassing educational levels from primary to high schools. Participant categorization was contingent upon respective age group, with a predominant focus on students at age range of 11–15 years. Notably, more than half of the studies (36 studies, accounting for 57.1%) were centred on participants in this age range. Following closely, another significant portion, comprising 23 studies (36.5%), targeted students in the 16-18-year students. It was noted that there are seven studies assessing students, covering two age range groups.

Task, Tests and Techniques of Assessing Scientific Inquiry

Components (facets) for assessing scientific inquiry.

In empirical studies selected, various assessment frameworks were introduced to evaluate scientific inquiry, each incorporating a diverse range of specific components. Zachos et al. ( 2000 ) considered scientific inquiry as multi-aspects of competence related to human cognitive characteristics. They employed hands-on performance assessment tasks, Floating and Sinking and the Period of Oscillation of a Pendulum, to assess students’ inquiry abilities within specific components: linking theory with evidence, formulating hypotheses, maintaining records, using appropriate or innovative laboratory materials, identifying cause-and-effect relationships, controlling experiments, and applying parsimony in drawing conclusions.

Cuevas and colleages ( 2005 ) developed contextual problem tasks to assess inquiry in five components: questioning, planning, implementing, concluding, and reporting. Their assessment task described a story about a child named Marie, who was trying to determine if the size of a container’s opening would influence the rate at which water evaporated. Students were asked to formulate a question reflecting the problem Marie was trying to solve, develop a hypothesis, design an investigation, list the materials needed, describe how to record results, and explain how to draw a conclusion. The framework were referred in a study by Turkan and Liu ( 2012 ) and later utilized in a study by Yang et al. ( 2016 ), where science inquiry was defined as comprising seven aspects of identifying a research question, formulating a hypothesis, designing an experimental procedure, planning necessary equipment and materials, collecting data and evidence, drawing evidence-based conclusions, and constructing conceptual understanding.

Other studies described inquiry as process skills (Kipnis & Hofstein, 2008 ), science process skills (Feyzíoglu, 2012 ; Temiz et al., 2006 ) and scientific process skills (Tosun, 2019 ). For example, Temiz et al. ( 2006 ) developed an instrument aimed to measure the development of 12 science process skills: formulating hypotheses, observing, manipulating materials, measuring, identifying and controlling variables, recording the data, demonstrating the ability to use numbers in space and time relationships, classifying, using the data to create models, predicting, interpreting data, and inferring information or solutions to problems. Meanwhile, an inquiry process framework of Kipnis and Hofstein ( 2008 ) included identifying problems, formulating hypotheses, designing an experiment, gathering and analysing data, and drawing conclusions about scientific problems and phenomena.

Furthermore, based on the previous studies (Gobert et al., 2013 ; Liu et al., 2008 ; Pine et al., 2006 ; Quellmalz et al., 2012 ; Zachos et al., 2000 ), Kuo et al., ( 2015 ) defined an inquiry proficiency framework to integrate cognitive skills with scientific knowledge during student participation in activities akin to scientific discovery. The framework emphasized four fundamental abilities as core components including questioning (e.g., asking and identifying questions), experimenting (e.g., identifying variables and planning experimental procedures), analysing (e.g., identifying relevant data and transforming data), and explaining (e.g., making a claim and using evidence). Their scenario-based tasks were created within a web-based application, covering four content areas (Physics, Chemistry, Biology, and Earth Science) across four inquiry abilities (Wu et al., 2015 ). Chi et al. ( 2019 ) defined scientific inquiry as the ability to integrate science knowledge and skills to identify scientific questions design and conduct investigation, analyse and interpret information and generate evidence-based explanations. A hands-on performance assessment instrument for measuring student scientific inquiry competences in the science lab was developed based on this framework (see a sample task in Fig. 5 a).

PISA 2015 developed the framework to assess 15-year-old students’ scientific inquiry competency of explaining phenomena, designing inquiry, interpreting data (OECD, 2017 ). Some empirical studies (e.g., Intasoi et al., 2020 ; Lin & Shie, 2024 ) developed assessment framework based on the framework to assess scientific inquiry competence of students. For example, Lin and Shie ( 2024 ) developed a PISA-type test to assess Grade 9 students’ scientific competence and knowledge related to curriculum and daily-life contexts (e.g., trolley motion, camping, household electricity, driving speed, etc.).

In the line, Arnold et al. ( 2018 ) referred to scientific inquiry as the competence to emphasize the cognitive aspects of the ability to use problem-solving procedures. Scientific competence was defined as the ability to understand, conduct, and critically evaluate scientific experiments on causal relationships, addressing problems and phenomena in the natural world. Three key sub-competences of experimentation were identified: generating hypotheses, designing experiments, and analysing data. Each sub-competence included five specific components. For instance, the sub-competence of generating hypotheses covered the ability to define the investigative problem, identify the relationship between dependent and independent variables to generate testable hypotheses or predictions and justify them, as well as propose different independent variables or alternative predictions. Zheng et al. ( 2022 ) categorized inquiry into eight components, highlighting information processing and reflective evaluation, echoed in study by Mutlu ( 2020 ).

In other approaches, Nowak et al. ( 2013 ) developed a model for assessing students’ inquiry ability, which had two dimensions: scientific reasoning (including question and hypothesis, plan and performance, and analysis and reflection) and inquiry methods (comprising modelling, experimenting, observing, comparing, and arranging). Together, these dimensions form a 9-cell matrix. Based on the theoretical structure, they developed a test instrument to assess students’ scientific inquiry (see sample item in Fig. 5 b). Meanwhile, Pedaste and colleages ( 2021 ) developed a science inquiry test for primary students based on the four-stage inquiry-based learning framework by Pedaste et al. ( 2015 ). The test encompassed the essential skills aligned with the four stages of the inquiry-based learning framework. These included analytical skills, which are primarily required in the Orientation, Conceptualization, and Investigation phases; planning skills, mainly needed in the Investigation phase; and interpretation skills, primarily needed in the Conclusion and Discussion phases.

Samples of the item/task for assessing scientific inquiry

A virtual experimentation environment developed by McElhaney and Linn ( 2011 ) simulated the experimentation activities of Airbags. These activities illustrated the interaction between the airbag and the driver during a head-on collision, using the steering wheel as a point of reference. Referred the existing studies (e.g., Kind, 2013 ; Liu et al., 2008 ; Pine et al., 2006 ), a simulation-based test developed by Wu et al. ( 2014 ) focused on two types of abilities: experimental and explaining. Experimental ability involved three sub-abilities: identifying and choosing variables, planning an experiment and selecting appropriate measurements, while explaining ability covered three sub-abilities: making a claim, using evidence, and evaluating alternative explanations. They designed four simulation tasks, namely Camera, Viscosity, Buoyancy and Flypaper. For example, the Flypaper task simulated a farm context in which students investigated which colour of flypaper could catch the most fruit flies. They were asked to propose hypotheses related to the decrease in flies according to the given chart, conduct appropriate experiments to measure the effect of the flypaper colour, investigate which colour of flypaper is best for catching fruit flies, and decide on alternative explanations based on the data evidence.

In the vein, Sui et al. ( 2024 ) designed an animation-based web application allow students conduct a scientific inquiry on atmospheric chemistry with animation experiments to understand the climate change and atmospheric chemistry. The scientific inquiry was defined with three core abilities: data analytic, control of variables and scientific reasoning. The digital game-based inquiry, BioScientist (Bónus et al., 2024 ) involved series of tasks, which focused on inquiry skills focusing on design of experiment, identification and control of variables, interpretation of data, and conclusion. For instance, a simulation provided some relevant variables, students need to manipulate the first one and then second variables to generate the data set. Based on the data-based evidence, they selected the answer and draw reasonable conclusions.

In summary, what becomes clear is that the mainstream framing of the construct of scientific inquiry was categorised as lists of specific components of competence. The frameworks for assessing scientific inquiry in technology-rich environments share many similarities with those used in traditional settings. In this view, it may summarise scientific competence into four main sub-competencies and their respective components (facets) based on the existing frameworks, as shown in Table 1 .

The Frequent Usage of the Components in Assessing Scientific Inquiry

In this section, we employed ENA to quantitatively visualize the usage frequency of yed ENA to quantitatively visualize the usage frequency of individual components and their co-usage with others in the selected empirical studies. Figure 6 illustrates the frequency of usage (represented by the size of the nodes) and the degree of co-usage of the components (represented by the width of the lines) across the reviewed studies.

In general, it appears that the nine facets were most often used to assess scientific inquiry, including formulate prediction or hypotheses (FP), formulate questions (FQ), set experiment (DS), vary independent variable (DV), measure dependent variable (DM), control confounding variables (DC), describe data (AD), interpret data (AI), and reach reasonable conclusion (CR). Other components were frequently used in inquiry tasks, including identify independent variable (FI), Identify dependent variable (FD), using appropriate method (AU) and evaluate methods (CE).

The pattern of components of scientific inquiry competence in selected studies simulated in the ENA model

Foundation Frameworks for Scientific Inquiry Assessment

To explore foundational frameworks for scientific inquiry assessment, we employed the Bibliometric analyses via the co-citation networks prevalent in the studies selected. The findings as depicted in Fig. 7 showed that US science education standards (NRC, 1996 ) stood out as the most frequently cited, followed by NRC texts A Framework for K-12 Science Education: Practices, Crosscutting Concepts, and Core Ideas” (2012) and “Inquiry and the National Science Education Standards: A Guide for Teaching and Learning” (NRC, 2000 ). Other texts were often cited such as: “The development of scientific thinking skills in elementary and middle school” (Zimmerman, 2007 ) and “Next Generation Science Standards: For States, By States” (2013). It is clear that the 1996 NRC standards were prominently featured in the discussion, while the 2012 framework was referred to more frequently than the actual standards, particularly in terms of citations in the reviewed studies.

The co-citation networks found in the studies reviewed

Constructs, Formats and Techniques Approaches in Assessing Scientific Inquiry

Generally, three types of tests emerged within the realm of scientific inquiry assessment: hands-on performance assessment, a battery of independent tests (paper battery), and digital-based battery tests (online battery) and simulation performance assessment. The analysis revealed that paper battery (41.1%) and on-line battery tests (39.7%) were the most widely applied construct, followed by and simulation performance assessment (37.0%). Hands-on performance (17.6%) still continues to hold its place in the field. The findings also suggest that, regardless of the mode of assessment, multiple-choice (71.4%) and open-ended (69.8%) formats are consistently prevalent. Notably, several studies (44.5%) used a combination of multiple-choice and open-ended formats.

Assessment of Scientific Inquiry in Traditional Environment

Performance assessments represent a groundwork approach to measuring students’ capabilities in scientific investigation, conceptualization, and problem-solving within authentic contexts. Researchers explored various dimensions of hands-on performance assessments, designing tasks that authentically mirror the scientific process. For example, Zachos et al. ( 2000 ) developed performance tasks mirroring scientific inquiry processes, assessing concepts, data collection, and conclusion drawing. Pine et al. ( 2006 ) emphasized inquiry skills like planning and data interpretation. Emden and Sumfleth ( 2016 ) assessed students’ ability in generating ideas, planning experiments, and drawing conclusions through hands-on inquiry tasks. They used video analysis in combined with paper-pencil free response reports to measure performance.

Traditional assessments tend to rely on standardized tests, featuring multiple-choice items aligned with policy-led standards. These tests, often administered in a paper-and-pencil format, measure students’ proficiency levels in comparison with peers. Without the need for advanced technology, they covered a wide range of content and question types, including multiple-choice, short answer, and essays (Fig. 8 ). The majority of studies employed such a battery of independent tests to assess one or more components of scientific inquiry (e.g., Arnold et al., 2018 ; Kaberman & Dori, 2009 ; Kazeni et al., 2018 ; Lin & Shie, 2024 ; Nowak et al., 2013 ; Schwichow et al., 2016 ; Vo et al., 2023 ; Van Vo & Csapó, 2021 ). There were a significant positive correlations between the paper-and-pencil tests and performance assessment tasks (e.g., Kruit et al., 2018 ). Table 2 presents an excerpt from the summarised table of the studies selected (See more Supplemental material at https://osf.io/5bt82 ).

Samples of item/task assessing scientific inquiry in paper-based modality. a A sample item in requiring interpretation [source from Kruit et al. ( 2018 )]. b A sample of a task for assessing inquiry [source from Temiz et al. ( 2006 )]

Assessment of Scientific Inquiry in Digital Based Environments

From 2012 onwards, studies started to increasingly use advanced technologies in digital-based environments in their assessment of scientific inquiry. Studies (e.g., Gobert et al., 2013 ; Kuo et al., 2015 ; Quellmalz et al., 2012 ; Sui et al., 2024 ) started to use innovative tools and methodologies to construct assessment platforms that more accurately captured the nuanced complexities of scientific inquiry. These include dynamic simulations with web-based applications like (Quellmalz et al., 2012 , 2013 ), Inquiry Intelligent Tutoring System (Inq-ITS) (Gobert et al., 2013 ), 3D-game simulation (Hickey et al., 2009 ; Ketelhut et al., 2013 ), PISA 2015 (e.g., OECD, 2017 ; Teig et al., 2020 ) (see Fig. 9 ) and scenario-based tasks integrating multimedia elements (Kuo et al., 2015 ). For example, Inq-ITS is an online intelligent tutoring and assessment platform designed for physics, life science, and earth science. It aims to automatically evaluate scientific inquiry skills in real-time through interactive microworld simulations.

Simulation-based tools like Simulation-based assessment of scientific inquiry abilities (Wu et al., 2014 ; Wu & Wu, 2020 ) can effectively assess abilities in explaining and other relevant components. Immersive virtual settings and automated content scoring engines offered efficient evaluation methods (Baker et al., 2016 ; Liu et al., 2016 ; Scalise & Clarke-Midura, 2018 ; Sui et al., 2024 ) and were potential for formative assessment (Hickey et al., 2009 ). The digital game-based inquiry, i.e., BioScientist (Bónus et al., 2024 ), Quest Atlantis (Hickey et al., 2009 ), allowed students to engage with a series of tasks, which focused on inquiry skills using simulation in which students interacted with suitable elements during the inquiry process. Table 3 illustrates an excerpt regarding components, tools and techniques in digital-based scientific inquiry assessment (See Supplemental material at https://osf.io/5bt82 ).

A screenshot of item 3 of Task 1 from the PISA 2015 item from the Running in Hot Weather unit [Source from OECD ( 2015 )]

Techniques for Developing and Validating Scientific Inquiry Assessment

Most studies referred to the American Education Research Association (AERA, 1999 ) for developing and validating scientific inquiry assessment tasks. This included defining the assessment framework, designing tasks and items, scoring rubrics, and conducting a pilot test (Arnold et al., 2018 ; Kuo et al., 2015 ; Lin & Shie, 2024 ; Lin et al., 2016 ; Nowak et al., 2013 ; Schwichow et al., 2016 ; Vo & Csapó, 2023 ).

Numerous methods and techniques were employed for scoring proficiency in assessing scientific inquiry. Full credit was applied to correct answers in multiple-choice tests and partial credit to score open-ended questions (Arnold et al., 2018 ; Kaberman & Dori, 2009 ; OECD, 2017 ; Sui et al., 2024 ; Teig et al., 2020 ). Interestingly, a high percentage of studies, as much as 36.8%, utilized a 3-point scale rubric in their assessments or evaluations (Intasoi et al., 2020 ). Log-file techniques were increasingly popular for assessing scientific inquiry in recent studies (Baker et al., 2016 ; McElhaney & Linn, 2011 ; Teig, 2024 ; Teig et al., 2020 ). Data-mining algorithms enhanced assessment accuracy (Gobert et al., 2015 ). Virtual Performance Assessments allowed to record a log data (Baker et al., 2016 ), containing students’ actions (e.g., clicks, double clicks, slider movements, drag and drop, changes in the text area) along with the timestamp for each action. Different actions and their timings were combined to reveal behavioural indicators, such as number of actions, number of trials, time before the first action, response time for each item, and total time for each unit. The process of assessment development and validation was found to be based on a construct modelling approach (Brown & Wilson, 2011 ; Kuo et al., 2015 ).

For validation approaches, the face validity of the test instrument was established based on faculty and student feedback (Kuo et al., 2015 ) or expert judgments (Šmida et al., 2024 ; Vo & Csapó, 2023 ; Wu et al., 2014 ). Construct validity focused on the test score as a measure of the psychological properties of the instrument. For validity analysis, most studies applied Rasch measurement model (Arnold et al., 2018 ; Chi et al., 2019 ; Intasoi et al., 2020 ; Kuo et al., 2015 ; Lin & Shie, 2024 ; Liu et al., 2008 ; Nowak et al., 2013 ; Pedaste et al., 2021 ; Quellmalz et al., 2013 ; Scalise & Clarke-Midura, 2018 ; Schwichow et al., 2016 ; Vo & Csapó, 2023 ; Wu et al., 2015 ), followed by factor analyses (Feyzíoglu, 2012 ; Lou et al., 2015 ; Pedaste et al., 2021 ; Samarapungavan et al., 2009 ; Šmida et al., 2024 ; Tosun, 2019 ). Predictive or criterion-related validity was used to demonstrate that the test scores are dependent on other variables, tests, or outcome criteria. In assessment of scientific inquiry, predictive validity referred to some standard tests, such as Lawson’s Classroom Test of Scientific Inquiry (e.g., Kuo et al., 2015 ; Wu et al., 2014 ), Louisiana Educational Assessment Program (e.g., Lou et al., 2015 ), General cognitive ability (e.g., Dori et al., 2018 ; Kruit et al., 2018 ) and science grades in school (Pedaste et al., 2021 ).

Most popular software employed for data analysis including the R (Sui et al., 2024 ; Van Vo & Csapó, 2021 ), ConQuest (Kuo et al., 2015 ; Lin & Shie, 2024 ; Nowak et al., 2013 ; Seeratan et al., 2020 ; Vo & Csapó, 2021 ), SPSS (Bónus et al., 2024 ; Temiz et al., 2006 ) and Winsteps (Arnold et al., 2018 ; Chi et al., 2019 ; Pedaste et al., 2021 ), and LISREL (Tosun, 2019 ).

Developmental Trend in Assessing Scientific Inquiry

The objective here was to investigate the evolving trends and patterns of scientific inquiry employed within the studies over time. The articles were sub-divided into two distinct temporal spans − 2000–2012 and 2013–2024. Figure 10 visualizes patterns of components of scientific inquiry competence which were used the studies in the 2000–2012 period (Fig. 10 a), the 2013–2024 period (Fig. 10 b) and a comparison of that between the two periods (Fig. 10 c). The graph of comparison was calculated by subtracting the weight of each connection in one network from the corresponding connections in another.

The results revealed that some main components, i.e., measure dependent variable (DM), reach reasonable conclusion (CR), identify independent variable (FI), set experiment (DS), control confounding variables (DC), vary independent variable (DV), identify dependent variable (FD), and formulate prediction (FP), were often used consistently over time. However, components such as using appropriate method (AU), evaluate methods (CE), defining task time (DT), defining replication (DR), and recognizing limitations (CL) demonstrated a heightened prevalence in the later period, indicating a heightened emphasis on these aspects of assessing scientific inquiry. Conversely, when examining the earlier period (2000–2012), components like identify independent variable (FI) and justify question / hypothesis (FJ) exhibited a more noticeable frequency of application.

Patterns of facets of scientific inquiry competence in selected studies simulated in the ENA model

To streamline the understanding of these tests in the scientific inquiry tasks, we employed co-occurrence networks adapted in Bibliometric analysis. The analysis revealed that battery independent tests and performance assessment are most frequently used with multiple-choice and open-ended constructs. However, the trend is toward the online and simulation ones with new techniques of log-file tracking and scaffolding (Figure 11 a).

When it comes to emphasizing vision in science education, empirical evidence has shown that the design of inquiry tests incorporated both the content of pure science, vision I scientific literacy, and the science-in-context applications related to science, vision II scientific literacy. This ensures a balanced evaluation that covers fundamental scientific principles as well as their real-world applications. However, it is noteworthy that recent studies have shown a growing preference for assessing scientific inquiry within science-in-context (Figure 11 b).

Trend of types and formats in assessing scientific inquiry. a Co-occurrence networks depicting types, formats and “vision” emphasis. b Types, formats and “vision” emphasis over time

Discussion and Conclusions

The paper utilized the PRISMA guideline for systematic review in combination with bibliometric analyses for reviewing scientific research literature to draw together a detailed overview of research on assessing scientific inquiry abilities in global educational settings.

Our review of the problem of assessing scientific inquiry allowed us illuminate this rapidly changing area of research. In the last two decades, while research on curriculum reforms in science inquiry-orientations have proceeded apace, research on digital modes of assessing scientific inquiry have only recently started to make an impact. Our analysis of sixty-three studies showed that scientific inquiry has been emphasized, integrated, and assessed in the settings of science education around the world. The bulk of this research, started in the US, was brought to global significance through the influence of transnational policy decision-makers, such as the OECD and mainly US-led networks of researchers. The US researchers published several academic papers in the earliest part of the timeline studied, and their findings remain today as foundational citations. This research was quickly followed by new networks forming from Germany, Turkey, Taiwan and China. Co-citation networks revealed that the US National Science Education Standards (NRC, 1996 ) remains as a foundational reference, even though the 2012 document should have had nearly equal significance. Surprisingly, the American Association for the Advancement of Science (AAAS) benchmarks were not cited as frequently in the case.

Over two decades, performance assessments and batteries of independent tests, employing both multiple-choice and open-ended formats, continue to be widely used for assessing scientific inquiry. Hands-on performance assessment remains one of the main modes of assessing competence in scientific inquiry. Moreover, a traditional written test can be easily administered, reliably scored, and is familiar to students, but falls short in effectively capturing the dynamics of real-life inquiry and may be significantly influenced by reading proficiency (Kruit et al., 2018 ). Besides, hands-on performance assessment is not efficient for large-scale assessments (Kuo et al., 2015 ). Therefore, there is a growing emphasis on developing authentic tests. These tests, which may include manipulatives, are considered to provide a more comprehensive assessment of students’ capability in conducting scientific inquiry through multiple formats (e.g., open-constructed, multiple-choice, multiple-true-false, short closed-constructed).

Our analysis showed that original components like formulating questions or hypotheses, designing experiments, analysing data, and drawing conclusions were consistently used for assessing scientific inquiry capabilities over time. However, certain sub-components, such as formulating prediction or hypotheses , formulating questions , setting experiment , varying independent variable , measuring dependent variable , controlling confounding variables , describing data , interpreting data , and reaching reasonable conclusions , were the most frequently used competences in the selected studies. Meanwhile, facets like specifying test time , defining replication , and recognizing limitations were shown to have an increasing prevalence in the last decade. This trend signals a possible enhanced emphasis on these facets or sub-components of scientific inquiry, particularly in digital-based environments. The growing focus on these areas may reflect the advancements in technology that allow for more precise measurement and analysis, thereby promoting a more rigorous approach to scientific inquiry.

In the last decade, online battery tests and simulation performance assessments have gained increasing popularity. These studies reflect the design and enactment of innovative assessments using advanced technology, such as Web-based Inquiry Science Environments (McElhaney & Linn, 2011 ), SimScientists (Quellmalz et al., 2012 , 2013 ), iSA–Earth Science (Lou et al., 2015 ), Multimedia-based assessment of scientific inquiry abilities (Kuo et al., 2015 ; Wu et al., 2015 ), Inq-ITS system (Inquiry Intelligent Tutoring System (Gobert et al., 2013 , 2015 ), Virtual Performance Assessments (Baker et al., 2016 ), Dynamic visualization to design animation-based activities (Sui et al., 2024 ).

In terms of emphasizing vision in science education, empirical evidence demonstrated that the design of inquiry tests included pure science content (vision I) and science-in-context considerations (vision II). However, recent studies increasingly preferred assessing scientific inquiry within real-world contexts. This trend reflects an understanding of the importance of students being able to apply scientific concepts to real-world problems, thus preparing them for the complex, interdisciplinary challenges they are likely to face in their futures. By focusing on context, these studies aim to enhance students’ ability to think critically and engage with science in a way that is relevant to their everyday lives and broader community issues. These are also partly reflected in alignment with national and international frameworks.

Implications

The paper not only identifies various aspects of studies and research within a specific field of assessing inquiry competence, but also provides systematic rationales related to the construction of the tools, tasks and techniques used to assess scientific inquiry capabilities in educational settings. This is valuable for science teachers as they create inquiry-oriented tasks in their classrooms. Additionally, new researchers can gain an overview of the research teams working in this area.

The foreseeable trend may be that the move towards dynamic and interactive inquiry assessments enables researchers to examine not just the accuracy of students’ responses (product data) but also the procedures and actions they employ to arrive at responses (process data) (Teig, 2024 ). Multi-faceted aspects of scientific inquiry can be observed during assessment tasks. Beside traditional components in formulating questions or hypotheses , designing experiments , analysing data , and drawing conclusions , some new aspects like task time , replication and recognizing limitations seem to more consider as they become measurable in technology-rich environment. Therefore, log-file analysis will be more popular approach in the field.

The development of scientific inquiry assessments should be considered as a multifaceted process of construct modelling. The combination of multiple validity approaches is encouraged in development of the assessment of scientific inquiry. Psychometric analysis through Rasch model is often employed in validating and scaling student performance. Alternative approaches to deal with log-file records are still in the early pioneering stages of development (e.g., Baker et al., 2016 ; McElhaney & Linn, 2011 ; Teig, 2024 ; Teig et al., 2020 ). An automated scoring engine demonstrated a promising approach to scoring constructed-response in assessment of inquiry ability (Liu et al., 2016 ). This opens a potential space for upcoming new research in this field with application of artificial intelligence.

The review illuminates the evolving landscape of scientific inquiry assessment development and validation, emphasizing the importance of a comprehensive and flexible approach to meet the diverse needs of educational and research settings. However, tackling such novel tasks necessitated not only an understanding of scientific inquiry assessment but also sophisticated technology and its corresponding infrastructures. For example, simulation tasks addressing complex real-world problems, such as climate change, water shortages, and global food security, necessitate the collaboration of various relevant stakeholders. It is crucial for research and educational technology institutions to play supportive roles for science teachers. More robust and published research on scientist-led K-12 outreach is essential for enhancing comprehension among scientists and K-12 stakeholders regarding the optimal practices and challenges associated with outreach initiatives (Abramowitz et al., 2024 ).

Science teachers were encouraged to integrate both pure science content and science-in-context applications into their teaching and assessment (Roberts & Bybee, 2014 ). This will involve teachers’ designing inquiry-based activities that apply scientific principles to real-world problems, helping students develop higher-order critical thinking skills and preparing them for future interdisciplinary challenges. Emphasizing real-world applications of scientific inquiry can help to make science education more relevant and engaging for students.

Moreover, the adoption of combined approaches to the literature review, integrating bibliometric and ENA analyses with systematic review PRISMA guidelines, demonstrates a meticulous and systematic approach to data synthesis. Beyond its immediate application here, this research design may serve as a model for future research endeavours, contributing to the advancement of novel methodologies.

Limitation of the Review

The review conducted here was limited to 63 empirical studies published in SCOPUS/WoS data between 2000 and 2024 and in English. It may not cover the full range of academic documents that are made available in other academic databases, potentially missing significant studies published in different languages or within other research repositories.

The nature of psychological issues is often controversial, and our suggested framework for assessing scientific inquiry competence is merely one of several approaches presented in the literature. Different scholars proposed various models, each with its own strengths and limitations, reflecting the ongoing debate and complexity within this field. Furthermore, the selection of articles was conducted and scored by the authors, which introduces the possibility of certain biases. These biases may stem from subjective interpretations, or unintentional preferences, potentially influencing the overall findings.

The application of advanced technology is sophisticated and diverse; we have highlighted only a few features without covering all aspects of digital-based assessment. Therefore, generalizations from the study need to be approached with caution. However, the study provides valuable insights into the fast-globalizing landscape of assessing scientific inquiry and will be of interest to researchers, educators, teachers in science education and those with an interest in grappling with similar problems of assessment.

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designing an experiment, collecting and analyzing data, and drawing a conclusion. This is sometimes also referred to as scientific inquiry. A hypothesis is a possible explanation for an observation. A good scientist will design a controlled experiment to test their hypothesis. In a controlled experiment, only one variable is tested at a time.
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Search. A controlled experiment aims to demonstrate causation between variables by manipulating an independent variable while controlling all other factors that could influence the results. Its purpose is to show that changes in one variable (the independent variable) directly cause changes in another variable (the dependent variable).
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In fact, this had been known since the 1930s, when controlled experiments showed that elements of tobacco smoke, when put into liquid form, could cause tumors to form on the skin of rabbits. Throughout the 1900s, several lines of evidence confirmed the tobacco/cancer link. These lines of evidence included.
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Summary of steps. Step 1: begin with a testable hypothesis. Step 2: explicitly state the independent variables. Step 3: carefully choose the dependent variables. step 4: consider possible nuisance variables & determine mitigation approach. Step 5: design the task to be performed. Step 6: design experiment protocol.
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Control in experiments is critical for internal validity, which allows you to establish a cause-and-effect relationship between variables. Example: Experiment. You're studying the effects of colours in advertising. You want to test whether using green for advertising fast food chains increases the value of their products.
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Draw conclusions and report the outcome of the experiment. ... Controlled experiment: A controlled experiment compares two groups of samples that differ only in independent variable. For example, a drug trial compares the effect of a group taking a placebo (control group) against those getting the drug (the treatment group). Experiments in a ...
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A controlled experiment allows you to isolate and study the clear result that will eventually allow you to draw conclusions. A single phenomenon is the result of multiple factors, but how do you know the independent effect of each factor? A controlled experiment basically limits the scope of the result because only one or two factors affecting ...
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A true experiment (a.k.a. a controlled experiment) always includes at least one control group that doesn't receive the experimental treatment.. However, some experiments use a within-subjects design to test treatments without a control group. In these designs, you usually compare one group's outcomes before and after a treatment (instead of comparing outcomes between different groups).
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A controlled experiment is a research study in which participants are randomly assigned to experimental and control groups. A controlled experiment allows researchers to determine cause and effect between variables. One drawback of controlled experiments is that they lack external validity (which means their results may not generalize to real ...
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The steps are: Ask a question. Research the topic. Develop a hypothesis. Test with an experiment. Analyze data. Report conclusions. Developing a controlled experiment is one way to test a ...
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Controlled Experiment. A controlled experiment is simply an experiment in which all factors are held constant except for one: the independent variable. A common type of controlled experiment compares a control group against an experimental group. All variables are identical between the two groups except for the factor being tested.
Chapter 1 Study Questions Flashcards
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Controlled Experiment
controlled experiment. Inquiry in which the researcher manipulates the independent variable (s), with some control over other factors, in order to observe the effects on the dependent variable (s). critical experiment. An experiment that provides a definitive test of two or more competing theories. experiment.
Assessing Scientific Inquiry: A Systematic Literature Review ...
Four core categories (formulating questions/hypotheses, designing experiments, analysing data, and drawing conclusions) were most often used with nine specific components (formulate questions formulate prediction/hypotheses, set experiment, vary independent variable, measure dependent variable, control confounding variables, describe data ...

Controlled Experiments: Definition, Steps, Results, Uses

Importance of controlled experiments in various fields

Why Controlled Experiments Matter?

Minimizing Biases and Confounding Variables

Achieving Causal Inference

Planning a Controlled Experiment

Identifying Variables and Control Groups

Designing Experimental Procedures and Protocols

Conducting a Controlled Experiment

Data Collection Methods and Instruments

Monitoring and Maintaining Experimental Conditions

Analysing Results and Drawing Conclusions

Interpretation of Results

Implications and Potential Applications

Common Challenges and Solutions

Dealing with Sample Size and Statistical Power

Mitigating Unforeseen Variables

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Controlled Experiment

What is the control group?

What are extraneous variables?

Why conduct controlled experiments?

Key Terminology

Control Group

Ecological validity

Experimenter effects

Demand characteristics

Independent variable (IV)

Dependent variable (DV)

Extraneous variables (EV)

Confounding variables

Random Allocation

Order effects

What is the control in an experiment?

What is the purpose of controlling the environment when testing a hypothesis?

Why are hypotheses important to controlled experiments?

What is the experimental method?

Understanding Controlled Experiments

2. Interactive Reading: A Case Study: The link between cancer and smoking. Initial observations

3. Controlled Experiments: General Features

4. Scientific Method and Experimental Design Flashcards

5. A controlled experiment to test the smoking/cancer connection

6. What Happened Next

7. Checking Understanding Quiz

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9. Next steps (reading about “The Shameful Past…”)

Controlled Experiment

Designing A Controlled Experiment

Identifying The Research Question

Formulating The Hypothesis

Selecting The Variables

Developing The Control Group

Randomization And Sample Size

Conducting A Controlled Experiment

Analyzing And Interpreting Results

Data Analysis Techniques

Identifying Trends And Patterns

Drawing Conclusions

Evaluating The Validity And Reliability

Limitations And Considerations

Potential Biases

Ethical Considerations

Generalizability And External Validity

Limitations Of Control Groups

Addressing Confounding Variables

Frequently Asked Questions Of Controlled Experiment

What Is An Example Of A Controlled Study?

What Is The Difference Between Controlled And Uncontrolled Experiments?

What Is A Controlled Cause To Effect Experiment?

What Is A Controlled Experiment?

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