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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

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How to Write a Scientific Report | Step-by-Step Guide

Got to document an experiment but don't know how? In this post, we'll guide you step-by-step through how to write a scientific report and provide you with an example.

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Is your teacher expecting you to write an experimental report for every class experiment? Are you still unsure about how to write a scientific report properly? Don’t fear! We will guide you through all the parts of a scientific report, step-by-step.

How to write a scientific report:

• What is a scientific report
• General rules to write Scientific reports
• Syllabus dot point 
• Introduction/Background information
• Risk assessment

What is a scientific report?

A scientific report documents all aspects of an experimental investigation. This includes:

• The aim of the experiment
• The hypothesis
• An introduction to the relevant background theory
• The methods used
• The results
• A discussion of the results
• The conclusion

Scientific reports allow their readers to understand the experiment without doing it themselves. In addition, scientific reports give others the opportunity to check the methodology of the experiment to ensure the validity of the results.

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A scientific report is written in several stages. We write the introduction, aim, and hypothesis before performing the experiment, record the results during the experiment, and complete the discussion and conclusions after the experiment.

But, before we delve deeper into how to write a scientific report, we need to have a science experiment to write about! Read our 7 Simple Experiments You Can Do At Home article and see which one you want to do.

General rules about writing scientific reports

Learning how to write a scientific report is different from writing English essays or speeches!

You have to use:

• Passive voice (which you should avoid when writing for other subjects like English!)
• Past-tense language
• Headings and subheadings
• A pencil to draw scientific diagrams and graphs
• Simple and clear lines for scientific diagrams
• Tables and graphs where necessary

Structure of scientific reports:

Now that you know the general rules on how to write scientific reports, let’s look at the conventions for their structure!

The title should simply introduce what your experiment is about.

The Role of Light in Photosynthesis

2. Introduction/Background information

Write a paragraph that gives your readers background information to understand your experiment.

This includes explaining scientific theories, processes and other related knowledge.

Photosynthesis is a vital process for life. It occurs when plants intake carbon dioxide, water, and light, and results in the production of glucose and water. The light required for photosynthesis is absorbed by chlorophyll, the green pigment of plants, which is contained in the chloroplasts.

The glucose produced through photosynthesis is stored as starch, which is used as an energy source for the plant and its consumers.

The presence of starch in the leaves of a plant indicates that photosynthesis has occurred.

The aim identifies what is going to be tested in the experiment. This should be short, concise and clear.

The aim of the experiment is to test whether light is required for photosynthesis to occur.

4. Hypothesis

The hypothesis is a prediction of the outcome of the experiment. You have to use background information to make an educated prediction.

It is predicted that photosynthesis will occur only in leaves that are exposed to light and not in leaves that are not exposed to light. This will be indicated by the presence or absence of starch in the leaves.

5. Risk assessment

Identify the hazards associated with the experiment and provide a method to prevent or minimise the risks. A hazard is something that can cause harm, and the risk is the likelihood that harm will occur from the hazard.

A table is an excellent way to present your risk assessment.

Remember, you have to specify the  type of harm that can occur because of the hazard. It is not enough to simply identify the hazard.

• Do not write:  “Scissors are sharp”
• Instead, you have to write:  “Scissors are sharp and can cause injury”

The method has 3 parts:

• A list of every material used
• Steps of what you did in the experiment
• A scientific diagram of the experimental apparatus

Let’s break down what you need to do for each section.

6a. Materials

This must list every piece of equipment and material you used in the experiment.

Remember, you need to also specify the amount of each material you used.

• 1 geranium plant
• Aluminium foil
• 2 test tubes
• 1 test tube rack
• 1 pair of scissors
• 1 250 mL beaker
• 1 pair of forceps
• 1 10 mL measuring cylinder
• Iodine solution (5 mL)
• Methylated spirit (50ml)
• Boiling water
• 2 Petri dishes

The rule of thumb is that you should write the method in a clear way so that readers are able to repeat the experiment and get similar results.

Using a numbered list for the steps of your experimental procedure is much clearer than writing a whole paragraph of text.  The steps should:

• Be written in a sequential order, based on when they were performed.
• Specify any equipment that was used.
• Specify the quantity of any materials that were used.

You also need to use past tense and passive voice when you are writing your method. Scientific reports are supposed to show the readers what you did in the experiment, not what you will do.

• Aluminium foil was used to fully cover a leaf of the geranium plant. The plant was left in the sun for three days.
• On the third day, the covered leaf and 1 non-covered leaf were collected from the plant. The foil was removed from the covered leaf, and a 1 cm square was cut from each leaf using a pair of scissors.
• 150 mL of water was boiled in a kettle and poured into a 250 mL beaker.
• Using forceps, the 1 cm square of covered leaf was placed into the beaker of boiling water for 2 minutes. It was then placed in a test tube labelled “dark”.
• The water in the beaker was discarded and replaced with 150 mL of freshly boiled water.
• Using forceps, the 1 cm square non-covered leaf was placed into the beaker of boiling water for 2 minutes. It was then placed in a test tube labelled “light”
• 5 mL of methylated spirit was measured with a measuring cylinder and poured into each test tube so that the leaves were fully covered.
• The water in the beaker was replaced with 150 mL of freshly boiled water and both the “light” and “dark” test tubes were immersed in the beaker of boiling water for 5 minutes.
• The leaves were collected from each test tube with forceps, rinsed under cold running water, and placed onto separate labelled Petri dishes.
• 3 drops of iodine solution were added to each leaf.
• Both Petri dishes were placed side by side and observations were recorded.
• The experiment was repeated 5 times, and results were compared between different groups.

6c. Diagram

After you finish your steps, it is time to draw your scientific diagrams! Here are some rules for drawing scientific diagrams:

• Always use a pencil to draw your scientific diagrams.
• Use simple, sharp, 2D lines and shapes to draw your diagram. Don’t draw 3D shapes or use shading.
• Label everything in your diagram.
• Use thin, straight lines to label your diagram. Do not use arrows.
• Ensure that the label lines touch the outline of the equipment you are labelling and not cross over it or stop short of it
• The label lines should never cross over each other.
• Use a ruler for any straight lines in your diagram.
• Draw a sufficiently large diagram so all components can be seen clearly.

This is where you document the results of your experiment. The data that you record for your experiment will generally be qualitative and/or quantitative.

Qualitative data is data that relates to qualities and is based on observations (qualitative – quality). This type of data is descriptive and is recorded in words. For example, the colour changed from green to orange, or the liquid became hot.

Quantitative data refers to numerical data (quantitative – quantity). This type of data is recorded using numbers and is either measured or counted. For example, the plant grew 5.2 cm, or there were 5 frogs.

You also need to record your results in an appropriate way. Most of the time, a table is the best way to do this.

Here are some rules to using tables

• Use a pencil and a ruler to draw your table
• Draw neat and straight lines
• Ensure that the table is closed (connect all your lines)
• Don’t cross your lines (erase any lines that stick out of the table)
• Use appropriate columns and rows
• Properly name each column and row (including the units of measurement in brackets)
• Do not write your units in the body of your table (units belong in the header)
• Always include a title

Note : If your results require calculations, clearly write each step.

Observations of the effects of light on the amount of starch in plant leaves.

If quantitative data was recorded, the data is often also plotted on a graph.

8. Discussion

The discussion is where you analyse and interpret your results, and identify any experimental errors or possible areas of improvements.

You should divide your discussion as follows.

1. Trend in the results

Describe the ‘trend’ in your results. That is, the relationship you observed between your independent and dependent variables.

The independent variable is the variable that you are changing in the experiment. In this experiment, it is the amount of light that the leaves are exposed to.

The dependent variable is the variable that you are measuring in the experiment, In this experiment, it is the presence of starch in the leaves.

Explain how a particular result is achieved by referring to scientific knowledge, theories and any other scientific resources you find. 2. Scientific explanation: 

The presence of starch is indicated when the addition of iodine causes the leaf to turn dark purple. The results show that starch was present in the leaves that were exposed to light, while the leaves that were not exposed to light did not contain starch.

2. Scientific explanation:

Provide an explanation of the results using scientific knowledge, theories and any other scientific resources you find.

As starch is produced during photosynthesis, these results show that light plays a key role in photosynthesis.

3. Validity 

Validity refers to whether or not your results are valid. This can be done by examining your variables.

VA lidity =  VA riables

Identify the independent, dependent, controlled variables and the control experiment (if you have one).

The controlled variables are the variables that you keep the same across all tests e.g. the size of the leaf sample.

The control experiment is where you don’t apply an independent variable. It is untouched for the whole experiment.

Ensure that you never change more than one variable at a time!

The independent variable of the experiment was amount of light that the leaves were exposed to (the covered and uncovered geranium leaf), while the dependent variable was the presence of starch. The controlled variables were the size of the leaf sample, the duration of the experiment, the amount of time the solutions were heated, and the amount of iodine solution used.

4. Reliability 

Identify how you ensured the reliability of the results.

RE liability = RE petition

Show that you repeated your experiments, cross-checked your results with other groups or collated your results with the class.

The reliability of the results was ensured by repeating the experiment 5 times and comparing results with other groups. Since other groups obtained comparable results, the results are reliable.

5. Accuracy

Accuracy should be discussed if your results are in the form of quantitative data, and there is an accepted value for the result.

Accuracy would not be discussed for our example photosynthesis experiment as qualitative data was collected, however it would if we were measuring gravity using a pendulum:

The measured value of gravity was 9.8 m/s 2 , which is in agreement with the accepted value of 9.8 m/s 2 .

6. Possible improvements 

Identify any errors or risks found in the experiment and provide a method to improve it.

If there are none, then suggest new ways to improve the experimental design, and/or minimise error and risks.

Possible improvements could be made by including control experiments. For example, testing whether the iodine solution turns dark purple when added to water or methylated spirits. This would help to ensure that the purple colour observed in the experiments is due to the presence of starch in the leaves rather than impurities.

9. Conclusion

State whether the aim was achieved, and if your hypothesis was supported.

The aim of the investigation was achieved, and it was found that light is required for photosynthesis to occur. This was evidenced by the presence of starch in leaves that had been exposed to light, and the absence of starch in leaves that had been unexposed. These results support the proposed hypothesis.

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What Is an Experiment? Definition and Design

The Basics of an Experiment

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• Ph.D., Biomedical Sciences, University of Tennessee at Knoxville
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Science is concerned with experiments and experimentation, but do you know what exactly an experiment is? Here's a look at what an experiment is... and isn't!

Key Takeaways: Experiments

• An experiment is a procedure designed to test a hypothesis as part of the scientific method.
• The two key variables in any experiment are the independent and dependent variables. The independent variable is controlled or changed to test its effects on the dependent variable.
• Three key types of experiments are controlled experiments, field experiments, and natural experiments.

What Is an Experiment? The Short Answer

In its simplest form, an experiment is simply the test of a hypothesis . A hypothesis, in turn, is a proposed relationship or explanation of phenomena.

Experiment Basics

The experiment is the foundation of the scientific method , which is a systematic means of exploring the world around you. Although some experiments take place in laboratories, you could perform an experiment anywhere, at any time.

Take a look at the steps of the scientific method:

• Make observations.
• Formulate a hypothesis.
• Design and conduct an experiment to test the hypothesis.
• Evaluate the results of the experiment.
• Accept or reject the hypothesis.
• If necessary, make and test a new hypothesis.

Types of Experiments

• Natural Experiments : A natural experiment also is called a quasi-experiment. A natural experiment involves making a prediction or forming a hypothesis and then gathering data by observing a system. The variables are not controlled in a natural experiment.
• Controlled Experiments : Lab experiments are controlled experiments , although you can perform a controlled experiment outside of a lab setting! In a controlled experiment, you compare an experimental group with a control group. Ideally, these two groups are identical except for one variable , the independent variable .
• Field Experiments : A field experiment may be either a natural experiment or a controlled experiment. It takes place in a real-world setting, rather than under lab conditions. For example, an experiment involving an animal in its natural habitat would be a field experiment.

Variables in an Experiment

Simply put, a variable is anything you can change or control in an experiment. Common examples of variables include temperature, duration of the experiment, composition of a material, amount of light, etc. There are three kinds of variables in an experiment: controlled variables, independent variables and dependent variables .

Controlled variables , sometimes called constant variables are variables that are kept constant or unchanging. For example, if you are doing an experiment measuring the fizz released from different types of soda, you might control the size of the container so that all brands of soda would be in 12-oz cans. If you are performing an experiment on the effect of spraying plants with different chemicals, you would try to maintain the same pressure and maybe the same volume when spraying your plants.

The independent variable is the one factor that you are changing. It is one factor because usually in an experiment you try to change one thing at a time. This makes measurements and interpretation of the data much easier. If you are trying to determine whether heating water allows you to dissolve more sugar in the water then your independent variable is the temperature of the water. This is the variable you are purposely controlling.

The dependent variable is the variable you observe, to see whether it is affected by your independent variable. In the example where you are heating water to see if this affects the amount of sugar you can dissolve , the mass or volume of sugar (whichever you choose to measure) would be your dependent variable.

Examples of Things That Are Not Experiments

• Making a model volcano.
• Making a poster.
• Changing a lot of factors at once, so you can't truly test the effect of the dependent variable.
• Trying something, just to see what happens. On the other hand, making observations or trying something, after making a prediction about what you expect will happen, is a type of experiment.
• Bailey, R.A. (2008). Design of Comparative Experiments . Cambridge: Cambridge University Press. ISBN 9780521683579.
• Beveridge, William I. B., The Art of Scientific Investigation . Heinemann, Melbourne, Australia, 1950.
• di Francia, G. Toraldo (1981). The Investigation of the Physical World . Cambridge University Press. ISBN 0-521-29925-X.
• Hinkelmann, Klaus and Kempthorne, Oscar (2008). Design and Analysis of Experiments, Volume I: Introduction to Experimental Design (Second ed.). Wiley. ISBN 978-0-471-72756-9.
• Shadish, William R.; Cook, Thomas D.; Campbell, Donald T. (2002). Experimental and quasi-experimental designs for generalized causal inference (Nachdr. ed.). Boston: Houghton Mifflin. ISBN 0-395-61556-9.
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Experimentation in Scientific Research: Variables and controls in practice

by Anthony Carpi, Ph.D., Anne E. Egger, Ph.D.

Listen to this reading

Did you know that experimental design was developed more than a thousand years ago by a Middle Eastern scientist who studied light? All of us use a form of experimental research in our day to day lives when we try to find the spot with the best cell phone reception, try out new cooking recipes, and more. Scientific experiments are built on similar principles.

Experimentation is a research method in which one or more variables are consciously manipulated and the outcome or effect of that manipulation on other variables is observed.

Experimental designs often make use of controls that provide a measure of variability within a system and a check for sources of error.

Experimental methods are commonly applied to determine causal relationships or to quantify the magnitude of response of a variable.

Anyone who has used a cellular phone knows that certain situations require a bit of research: If you suddenly find yourself in an area with poor phone reception, you might move a bit to the left or right, walk a few steps forward or back, or even hold the phone over your head to get a better signal. While the actions of a cell phone user might seem obvious, the person seeking cell phone reception is actually performing a scientific experiment: consciously manipulating one component (the location of the cell phone) and observing the effect of that action on another component (the phone's reception). Scientific experiments are obviously a bit more complicated, and generally involve more rigorous use of controls , but they draw on the same type of reasoning that we use in many everyday situations. In fact, the earliest documented scientific experiments were devised to answer a very common everyday question: how vision works.

• A brief history of experimental methods

Figure 1: Alhazen (965-ca.1039) as pictured on an Iraqi 10,000-dinar note

One of the first ideas regarding how human vision works came from the Greek philosopher Empedocles around 450 BCE . Empedocles reasoned that the Greek goddess Aphrodite had lit a fire in the human eye, and vision was possible because light rays from this fire emanated from the eye, illuminating objects around us. While a number of people challenged this proposal, the idea that light radiated from the human eye proved surprisingly persistent until around 1,000 CE , when a Middle Eastern scientist advanced our knowledge of the nature of light and, in so doing, developed a new and more rigorous approach to scientific research . Abū 'Alī al-Hasan ibn al-Hasan ibn al-Haytham, also known as Alhazen , was born in 965 CE in the Arabian city of Basra in what is present-day Iraq. He began his scientific studies in physics, mathematics, and other sciences after reading the works of several Greek philosophers. One of Alhazen's most significant contributions was a seven-volume work on optics titled Kitab al-Manazir (later translated to Latin as Opticae Thesaurus Alhazeni – Alhazen's Book of Optics ). Beyond the contributions this book made to the field of optics, it was a remarkable work in that it based conclusions on experimental evidence rather than abstract reasoning – the first major publication to do so. Alhazen's contributions have proved so significant that his likeness was immortalized on the 2003 10,000-dinar note issued by Iraq (Figure 1).

Alhazen invested significant time studying light , color, shadows, rainbows, and other optical phenomena. Among this work was a study in which he stood in a darkened room with a small hole in one wall. Outside of the room, he hung two lanterns at different heights. Alhazen observed that the light from each lantern illuminated a different spot in the room, and each lighted spot formed a direct line with the hole and one of the lanterns outside the room. He also found that covering a lantern caused the spot it illuminated to darken, and exposing the lantern caused the spot to reappear. Thus, Alhazen provided some of the first experimental evidence that light does not emanate from the human eye but rather is emitted by certain objects (like lanterns) and travels from these objects in straight lines. Alhazen's experiment may seem simplistic today, but his methodology was groundbreaking: He developed a hypothesis based on observations of physical relationships (that light comes from objects), and then designed an experiment to test that hypothesis. Despite the simplicity of the method , Alhazen's experiment was a critical step in refuting the long-standing theory that light emanated from the human eye, and it was a major event in the development of modern scientific research methodology.

Comprehension Checkpoint

• Experimentation as a scientific research method

Experimentation is one scientific research method , perhaps the most recognizable, in a spectrum of methods that also includes description, comparison, and modeling (see our Description , Comparison , and Modeling modules). While all of these methods share in common a scientific approach, experimentation is unique in that it involves the conscious manipulation of certain aspects of a real system and the observation of the effects of that manipulation. You could solve a cell phone reception problem by walking around a neighborhood until you see a cell phone tower, observing other cell phone users to see where those people who get the best reception are standing, or looking on the web for a map of cell phone signal coverage. All of these methods could also provide answers, but by moving around and testing reception yourself, you are experimenting.

• Variables: Independent and dependent

In the experimental method , a condition or a parameter , generally referred to as a variable , is consciously manipulated (often referred to as a treatment) and the outcome or effect of that manipulation is observed on other variables. Variables are given different names depending on whether they are the ones manipulated or the ones observed:

• Independent variable refers to a condition within an experiment that is manipulated by the scientist.
• Dependent variable refers to an event or outcome of an experiment that might be affected by the manipulation of the independent variable .

Scientific experimentation helps to determine the nature of the relationship between independent and dependent variables . While it is often difficult, or sometimes impossible, to manipulate a single variable in an experiment , scientists often work to minimize the number of variables being manipulated. For example, as we move from one location to another to get better cell reception, we likely change the orientation of our body, perhaps from south-facing to east-facing, or we hold the cell phone at a different angle. Which variable affected reception: location, orientation, or angle of the phone? It is critical that scientists understand which aspects of their experiment they are manipulating so that they can accurately determine the impacts of that manipulation . In order to constrain the possible outcomes of an experimental procedure, most scientific experiments use a system of controls .

• Controls: Negative, positive, and placebos

In a controlled study, a scientist essentially runs two (or more) parallel and simultaneous experiments: a treatment group, in which the effect of an experimental manipulation is observed on a dependent variable , and a control group, which uses all of the same conditions as the first with the exception of the actual treatment. Controls can fall into one of two groups: negative controls and positive controls .

In a negative control , the control group is exposed to all of the experimental conditions except for the actual treatment . The need to match all experimental conditions exactly is so great that, for example, in a trial for a new drug, the negative control group will be given a pill or liquid that looks exactly like the drug, except that it will not contain the drug itself, a control often referred to as a placebo . Negative controls allow scientists to measure the natural variability of the dependent variable(s), provide a means of measuring error in the experiment , and also provide a baseline to measure against the experimental treatment.

Some experimental designs also make use of positive controls . A positive control is run as a parallel experiment and generally involves the use of an alternative treatment that the researcher knows will have an effect on the dependent variable . For example, when testing the effectiveness of a new drug for pain relief, a scientist might administer treatment placebo to one group of patients as a negative control , and a known treatment like aspirin to a separate group of individuals as a positive control since the pain-relieving aspects of aspirin are well documented. In both cases, the controls allow scientists to quantify background variability and reject alternative hypotheses that might otherwise explain the effect of the treatment on the dependent variable .

• Experimentation in practice: The case of Louis Pasteur

Well-controlled experiments generally provide strong evidence of causality, demonstrating whether the manipulation of one variable causes a response in another variable. For example, as early as the 6th century BCE , Anaximander , a Greek philosopher, speculated that life could be formed from a mixture of sea water, mud, and sunlight. The idea probably stemmed from the observation of worms, mosquitoes, and other insects "magically" appearing in mudflats and other shallow areas. While the suggestion was challenged on a number of occasions, the idea that living microorganisms could be spontaneously generated from air persisted until the middle of the 18 th century.

In the 1750s, John Needham, a Scottish clergyman and naturalist, claimed to have proved that spontaneous generation does occur when he showed that microorganisms flourished in certain foods such as soup broth, even after they had been briefly boiled and covered. Several years later, the Italian abbot and biologist Lazzaro Spallanzani , boiled soup broth for over an hour and then placed bowls of this soup in different conditions, sealing some and leaving others exposed to air. Spallanzani found that microorganisms grew in the soup exposed to air but were absent from the sealed soup. He therefore challenged Needham's conclusions and hypothesized that microorganisms suspended in air settled onto the exposed soup but not the sealed soup, and rejected the idea of spontaneous generation .

Needham countered, arguing that the growth of bacteria in the soup was not due to microbes settling onto the soup from the air, but rather because spontaneous generation required contact with an intangible "life force" in the air itself. He proposed that Spallanzani's extensive boiling destroyed the "life force" present in the soup, preventing spontaneous generation in the sealed bowls but allowing air to replenish the life force in the open bowls. For several decades, scientists continued to debate the spontaneous generation theory of life, with support for the theory coming from several notable scientists including Félix Pouchet and Henry Bastion. Pouchet, Director of the Rouen Museum of Natural History in France, and Bastion, a well-known British bacteriologist, argued that living organisms could spontaneously arise from chemical processes such as fermentation and putrefaction. The debate became so heated that in 1860, the French Academy of Sciences established the Alhumbert prize of 2,500 francs to the first person who could conclusively resolve the conflict. In 1864, Louis Pasteur achieved that result with a series of well-controlled experiments and in doing so claimed the Alhumbert prize.

Pasteur prepared for his experiments by studying the work of others that came before him. In fact, in April 1861 Pasteur wrote to Pouchet to obtain a research description that Pouchet had published. In this letter, Pasteur writes:

Paris, April 3, 1861 Dear Colleague, The difference of our opinions on the famous question of spontaneous generation does not prevent me from esteeming highly your labor and praiseworthy efforts... The sincerity of these sentiments...permits me to have recourse to your obligingness in full confidence. I read with great care everything that you write on the subject that occupies both of us. Now, I cannot obtain a brochure that I understand you have just published.... I would be happy to have a copy of it because I am at present editing the totality of my observations, where naturally I criticize your assertions. L. Pasteur (Porter, 1961)

Pasteur received the brochure from Pouchet several days later and went on to conduct his own experiments . In these, he repeated Spallanzani's method of boiling soup broth, but he divided the broth into portions and exposed these portions to different controlled conditions. Some broth was placed in flasks that had straight necks that were open to the air, some broth was placed in sealed flasks that were not open to the air, and some broth was placed into a specially designed set of swan-necked flasks, in which the broth would be open to the air but the air would have to travel a curved path before reaching the broth, thus preventing anything that might be present in the air from simply settling onto the soup (Figure 2). Pasteur then observed the response of the dependent variable (the growth of microorganisms) in response to the independent variable (the design of the flask). Pasteur's experiments contained both positive controls (samples in the straight-necked flasks that he knew would become contaminated with microorganisms) and negative controls (samples in the sealed flasks that he knew would remain sterile). If spontaneous generation did indeed occur upon exposure to air, Pasteur hypothesized, microorganisms would be found in both the swan-neck flasks and the straight-necked flasks, but not in the sealed flasks. Instead, Pasteur found that microorganisms appeared in the straight-necked flasks, but not in the sealed flasks or the swan-necked flasks.

Figure 2: Pasteur's drawings of the flasks he used (Pasteur, 1861). Fig. 25 D, C, and B (top) show various sealed flasks (negative controls); Fig. 26 (bottom right) illustrates a straight-necked flask directly open to the atmosphere (positive control); and Fig. 25 A (bottom left) illustrates the specially designed swan-necked flask (treatment group).

By using controls and replicating his experiment (he used more than one of each type of flask), Pasteur was able to answer many of the questions that still surrounded the issue of spontaneous generation. Pasteur said of his experimental design, "I affirm with the most perfect sincerity that I have never had a single experiment, arranged as I have just explained, which gave me a doubtful result" (Porter, 1961). Pasteur's work helped refute the theory of spontaneous generation – his experiments showed that air alone was not the cause of bacterial growth in the flask, and his research supported the hypothesis that live microorganisms suspended in air could settle onto the broth in open-necked flasks via gravity .

• Experimentation across disciplines

Experiments are used across all scientific disciplines to investigate a multitude of questions. In some cases, scientific experiments are used for exploratory purposes in which the scientist does not know what the dependent variable is. In this type of experiment, the scientist will manipulate an independent variable and observe what the effect of the manipulation is in order to identify a dependent variable (or variables). Exploratory experiments are sometimes used in nutritional biology when scientists probe the function and purpose of dietary nutrients . In one approach, a scientist will expose one group of animals to a normal diet, and a second group to a similar diet except that it is lacking a specific vitamin or nutrient. The researcher will then observe the two groups to see what specific physiological changes or medical problems arise in the group lacking the nutrient being studied.

Scientific experiments are also commonly used to quantify the magnitude of a relationship between two or more variables . For example, in the fields of pharmacology and toxicology, scientific experiments are used to determine the dose-response relationship of a new drug or chemical. In these approaches, researchers perform a series of experiments in which a population of organisms , such as laboratory mice, is separated into groups and each group is exposed to a different amount of the drug or chemical of interest. The analysis of the data that result from these experiments (see our Data Analysis and Interpretation module) involves comparing the degree of the organism's response to the dose of the substance administered.

In this context, experiments can provide additional evidence to complement other research methods . For example, in the 1950s a great debate ensued over whether or not the chemicals in cigarette smoke cause cancer. Several researchers had conducted comparative studies (see our Comparison in Scientific Research module) that indicated that patients who smoked had a higher probability of developing lung cancer when compared to nonsmokers. Comparative studies differ slightly from experimental methods in that you do not consciously manipulate a variable ; rather you observe differences between two or more groups depending on whether or not they fall into a treatment or control group. Cigarette companies and lobbyists criticized these studies, suggesting that the relationship between smoking and lung cancer was coincidental. Several researchers noted the need for a clear dose-response study; however, the difficulties in getting cigarette smoke into the lungs of laboratory animals prevented this research. In the mid-1950s, Ernest Wynder and colleagues had an ingenious idea: They condensed the chemicals from cigarette smoke into a liquid and applied this in various doses to the skin of groups of mice. The researchers published data from a dose-response experiment of the effect of tobacco smoke condensate on mice (Wynder et al., 1957).

As seen in Figure 3, the researchers found a positive relationship between the amount of condensate applied to the skin of mice and the number of cancers that developed. The graph shows the results of a study in which different groups of mice were exposed to increasing amounts of cigarette tar. The black dots indicate the percentage of each sample group of mice that developed cancer for a given amount cigarette smoke "condensate" applied to their skin. The vertical lines are error bars, showing the amount of uncertainty . The graph shows generally increasing cancer rates with greater exposure. This study was one of the first pieces of experimental evidence in the cigarette smoking debate , and it helped strengthen the case for cigarette smoke as the causative agent in lung cancer in smokers.

Figure 3: Percentage of mice with cancer versus the amount cigarette smoke "condensate" applied to their skin (source: Wynder et al., 1957).

Sometimes experimental approaches and other research methods are not clearly distinct, or scientists may even use multiple research approaches in combination. For example, at 1:52 a.m. EDT on July 4, 2005, scientists with the National Aeronautics and Space Administration (NASA) conducted a study in which a 370 kg spacecraft named Deep Impact was purposely slammed into passing comet Tempel 1. A nearby spacecraft observed the impact and radioed data back to Earth. The research was partially descriptive in that it documented the chemical composition of the comet, but it was also partly experimental in that the effect of slamming the Deep Impact probe into the comet on the volatilization of previously undetected compounds , such as water, was assessed (A'Hearn et al., 2005). It is particularly common that experimentation and description overlap: Another example is Jane Goodall 's research on the behavior of chimpanzees, which can be read in our Description in Scientific Research module.

• Limitations of experimental methods

Figure 4: An image of comet Tempel 1 67 seconds after collision with the Deep Impact impactor. Image credit: NASA/JPL-Caltech/UMD http://deepimpact.umd.edu/gallery/HRI_937_1.html

While scientific experiments provide invaluable data regarding causal relationships, they do have limitations. One criticism of experiments is that they do not necessarily represent real-world situations. In order to clearly identify the relationship between an independent variable and a dependent variable , experiments are designed so that many other contributing variables are fixed or eliminated. For example, in an experiment designed to quantify the effect of vitamin A dose on the metabolism of beta-carotene in humans, Shawna Lemke and colleagues had to precisely control the diet of their human volunteers (Lemke, Dueker et al. 2003). They asked their participants to limit their intake of foods rich in vitamin A and further asked that they maintain a precise log of all foods eaten for 1 week prior to their study. At the time of their study, they controlled their participants' diet by feeding them all the same meals, described in the methods section of their research article in this way:

Meals were controlled for time and content on the dose administration day. Lunch was served at 5.5 h postdosing and consisted of a frozen dinner (Enchiladas, Amy's Kitchen, Petaluma, CA), a blueberry bagel with jelly, 1 apple and 1 banana, and a large chocolate chunk cookie (Pepperidge Farm). Dinner was served 10.5 h post dose and consisted of a frozen dinner (Chinese Stir Fry, Amy's Kitchen) plus the bagel and fruit taken for lunch.

While this is an important aspect of making an experiment manageable and informative, it is often not representative of the real world, in which many variables may change at once, including the foods you eat. Still, experimental research is an excellent way of determining relationships between variables that can be later validated in real world settings through descriptive or comparative studies.

Design is critical to the success or failure of an experiment . Slight variations in the experimental set-up could strongly affect the outcome being measured. For example, during the 1950s, a number of experiments were conducted to evaluate the toxicity in mammals of the metal molybdenum, using rats as experimental subjects . Unexpectedly, these experiments seemed to indicate that the type of cage the rats were housed in affected the toxicity of molybdenum. In response, G. Brinkman and Russell Miller set up an experiment to investigate this observation (Brinkman & Miller, 1961). Brinkman and Miller fed two groups of rats a normal diet that was supplemented with 200 parts per million (ppm) of molybdenum. One group of rats was housed in galvanized steel (steel coated with zinc to reduce corrosion) cages and the second group was housed in stainless steel cages. Rats housed in the galvanized steel cages suffered more from molybdenum toxicity than the other group: They had higher concentrations of molybdenum in their livers and lower blood hemoglobin levels. It was then shown that when the rats chewed on their cages, those housed in the galvanized metal cages absorbed zinc plated onto the metal bars, and zinc is now known to affect the toxicity of molybdenum. In order to control for zinc exposure, then, stainless steel cages needed to be used for all rats.

Scientists also have an obligation to adhere to ethical limits in designing and conducting experiments . During World War II, doctors working in Nazi Germany conducted many heinous experiments using human subjects . Among them was an experiment meant to identify effective treatments for hypothermia in humans, in which concentration camp prisoners were forced to sit in ice water or left naked outdoors in freezing temperatures and then re-warmed by various means. Many of the exposed victims froze to death or suffered permanent injuries. As a result of the Nazi experiments and other unethical research , strict scientific ethical standards have been adopted by the United States and other governments, and by the scientific community at large. Among other things, ethical standards (see our Scientific Ethics module) require that the benefits of research outweigh the risks to human subjects, and those who participate do so voluntarily and only after they have been made fully aware of all the risks posed by the research. These guidelines have far-reaching effects: While the clearest indication of causation in the cigarette smoke and lung cancer debate would have been to design an experiment in which one group of people was asked to take up smoking and another group was asked to refrain from smoking, it would be highly unethical for a scientist to purposefully expose a group of healthy people to a suspected cancer causing agent. As an alternative, comparative studies (see our Comparison in Scientific Research module) were initiated in humans, and experimental studies focused on animal subjects. The combination of these and other studies provided even stronger evidence of the link between smoking and lung cancer than either one method alone would have.

• Experimentation in modern practice

Like all scientific research , the results of experiments are shared with the scientific community, are built upon, and inspire additional experiments and research. For example, once Alhazen established that light given off by objects enters the human eye, the natural question that was asked was "What is the nature of light that enters the human eye?" Two common theories about the nature of light were debated for many years. Sir Isaac Newton was among the principal proponents of a theory suggesting that light was made of small particles . The English naturalist Robert Hooke (who held the interesting title of Curator of Experiments at the Royal Society of London) supported a different theory stating that light was a type of wave, like sound waves . In 1801, Thomas Young conducted a now classic scientific experiment that helped resolve this controversy . Young, like Alhazen, worked in a darkened room and allowed light to enter only through a small hole in a window shade (Figure 5). Young refocused the beam of light with mirrors and split the beam with a paper-thin card. The split light beams were then projected onto a screen, and formed an alternating light and dark banding pattern – that was a sign that light was indeed a wave (see our Light I: Particle or Wave? module).

Figure 5: Young's split-light beam experiment helped clarify the wave nature of light.

Approximately 100 years later, in 1905, new experiments led Albert Einstein to conclude that light exhibits properties of both waves and particles . Einstein's dual wave-particle theory is now generally accepted by scientists.

Experiments continue to help refine our understanding of light even today. In addition to his wave-particle theory , Einstein also proposed that the speed of light was unchanging and absolute. Yet in 1998 a group of scientists led by Lene Hau showed that light could be slowed from its normal speed of 3 x 10 8 meters per second to a mere 17 meters per second with a special experimental apparatus (Hau et al., 1999). The series of experiments that began with Alhazen 's work 1000 years ago has led to a progressively deeper understanding of the nature of light. Although the tools with which scientists conduct experiments may have become more complex, the principles behind controlled experiments are remarkably similar to those used by Pasteur and Alhazen hundreds of years ago.

Table of Contents

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Science and the scientific method: Definitions and examples

Here's a look at the foundation of doing science — the scientific method.

The scientific method

Hypothesis, theory and law, a brief history of science, additional resources, bibliography.

Science is a systematic and logical approach to discovering how things in the universe work. It is also the body of knowledge accumulated through the discoveries about all the things in the universe. 

The word "science" is derived from the Latin word "scientia," which means knowledge based on demonstrable and reproducible data, according to the Merriam-Webster dictionary . True to this definition, science aims for measurable results through testing and analysis, a process known as the scientific method. Science is based on fact, not opinion or preferences. The process of science is designed to challenge ideas through research. One important aspect of the scientific process is that it focuses only on the natural world, according to the University of California, Berkeley . Anything that is considered supernatural, or beyond physical reality, does not fit into the definition of science.

When conducting research, scientists use the scientific method to collect measurable, empirical evidence in an experiment related to a hypothesis (often in the form of an if/then statement) that is designed to support or contradict a scientific theory .

"As a field biologist, my favorite part of the scientific method is being in the field collecting the data," Jaime Tanner, a professor of biology at Marlboro College, told Live Science. "But what really makes that fun is knowing that you are trying to answer an interesting question. So the first step in identifying questions and generating possible answers (hypotheses) is also very important and is a creative process. Then once you collect the data you analyze it to see if your hypothesis is supported or not."

The steps of the scientific method go something like this, according to Highline College :

• Make an observation or observations.
• Form a hypothesis — a tentative description of what's been observed, and make predictions based on that hypothesis.
• Test the hypothesis and predictions in an experiment that can be reproduced.
• Analyze the data and draw conclusions; accept or reject the hypothesis or modify the hypothesis if necessary.
• Reproduce the experiment until there are no discrepancies between observations and theory. "Replication of methods and results is my favorite step in the scientific method," Moshe Pritsker, a former post-doctoral researcher at Harvard Medical School and CEO of JoVE, told Live Science. "The reproducibility of published experiments is the foundation of science. No reproducibility — no science."

Some key underpinnings to the scientific method:

• The hypothesis must be testable and falsifiable, according to North Carolina State University . Falsifiable means that there must be a possible negative answer to the hypothesis.
• Research must involve deductive reasoning and inductive reasoning . Deductive reasoning is the process of using true premises to reach a logical true conclusion while inductive reasoning uses observations to infer an explanation for those observations.
• An experiment should include a dependent variable (which does not change) and an independent variable (which does change), according to the University of California, Santa Barbara .
• An experiment should include an experimental group and a control group. The control group is what the experimental group is compared against, according to Britannica .

The process of generating and testing a hypothesis forms the backbone of the scientific method. When an idea has been confirmed over many experiments, it can be called a scientific theory. While a theory provides an explanation for a phenomenon, a scientific law provides a description of a phenomenon, according to The University of Waikato . One example would be the law of conservation of energy, which is the first law of thermodynamics that says that energy can neither be created nor destroyed. 

A law describes an observed phenomenon, but it doesn't explain why the phenomenon exists or what causes it. "In science, laws are a starting place," said Peter Coppinger, an associate professor of biology and biomedical engineering at the Rose-Hulman Institute of Technology. "From there, scientists can then ask the questions, 'Why and how?'"

Laws are generally considered to be without exception, though some laws have been modified over time after further testing found discrepancies. For instance, Newton's laws of motion describe everything we've observed in the macroscopic world, but they break down at the subatomic level.

This does not mean theories are not meaningful. For a hypothesis to become a theory, scientists must conduct rigorous testing, typically across multiple disciplines by separate groups of scientists. Saying something is "just a theory" confuses the scientific definition of "theory" with the layperson's definition. To most people a theory is a hunch. In science, a theory is the framework for observations and facts, Tanner told Live Science.

The earliest evidence of science can be found as far back as records exist. Early tablets contain numerals and information about the solar system , which were derived by using careful observation, prediction and testing of those predictions. Science became decidedly more "scientific" over time, however.

1200s: Robert Grosseteste developed the framework for the proper methods of modern scientific experimentation, according to the Stanford Encyclopedia of Philosophy. His works included the principle that an inquiry must be based on measurable evidence that is confirmed through testing.

1400s: Leonardo da Vinci began his notebooks in pursuit of evidence that the human body is microcosmic. The artist, scientist and mathematician also gathered information about optics and hydrodynamics.

1500s: Nicolaus Copernicus advanced the understanding of the solar system with his discovery of heliocentrism. This is a model in which Earth and the other planets revolve around the sun, which is the center of the solar system.

1600s: Johannes Kepler built upon those observations with his laws of planetary motion. Galileo Galilei improved on a new invention, the telescope, and used it to study the sun and planets. The 1600s also saw advancements in the study of physics as Isaac Newton developed his laws of motion.

1700s: Benjamin Franklin discovered that lightning is electrical. He also contributed to the study of oceanography and meteorology. The understanding of chemistry also evolved during this century as Antoine Lavoisier, dubbed the father of modern chemistry , developed the law of conservation of mass.

1800s: Milestones included Alessandro Volta's discoveries regarding electrochemical series, which led to the invention of the battery. John Dalton also introduced atomic theory, which stated that all matter is composed of atoms that combine to form molecules. The basis of modern study of genetics advanced as Gregor Mendel unveiled his laws of inheritance. Later in the century, Wilhelm Conrad Röntgen discovered X-rays , while George Ohm's law provided the basis for understanding how to harness electrical charges.

1900s: The discoveries of Albert Einstein , who is best known for his theory of relativity, dominated the beginning of the 20th century. Einstein's theory of relativity is actually two separate theories. His special theory of relativity, which he outlined in a 1905 paper, " The Electrodynamics of Moving Bodies ," concluded that time must change according to the speed of a moving object relative to the frame of reference of an observer. His second theory of general relativity, which he published as " The Foundation of the General Theory of Relativity ," advanced the idea that matter causes space to curve.

In 1952, Jonas Salk developed the polio vaccine , which reduced the incidence of polio in the United States by nearly 90%, according to Britannica . The following year, James D. Watson and Francis Crick discovered the structure of DNA , which is a double helix formed by base pairs attached to a sugar-phosphate backbone, according to the National Human Genome Research Institute .

2000s: The 21st century saw the first draft of the human genome completed, leading to a greater understanding of DNA. This advanced the study of genetics, its role in human biology and its use as a predictor of diseases and other disorders, according to the National Human Genome Research Institute .

• This video from City University of New York delves into the basics of what defines science.
• Learn about what makes science science in this book excerpt from Washington State University .
• This resource from the University of Michigan — Flint explains how to design your own scientific study.

Merriam-Webster Dictionary, Scientia. 2022. https://www.merriam-webster.com/dictionary/scientia

University of California, Berkeley, "Understanding Science: An Overview." 2022. ​​ https://undsci.berkeley.edu/article/0_0_0/intro_01  

Highline College, "Scientific method." July 12, 2015. https://people.highline.edu/iglozman/classes/astronotes/scimeth.htm  

North Carolina State University, "Science Scripts." https://projects.ncsu.edu/project/bio183de/Black/science/science_scripts.html  

University of California, Santa Barbara. "What is an Independent variable?" October 31,2017. http://scienceline.ucsb.edu/getkey.php?key=6045  

Encyclopedia Britannica, "Control group." May 14, 2020. https://www.britannica.com/science/control-group  

The University of Waikato, "Scientific Hypothesis, Theories and Laws." https://sci.waikato.ac.nz/evolution/Theories.shtml  

Stanford Encyclopedia of Philosophy, Robert Grosseteste. May 3, 2019. https://plato.stanford.edu/entries/grosseteste/  

Encyclopedia Britannica, "Jonas Salk." October 21, 2021. https://www.britannica.com/ biography /Jonas-Salk

National Human Genome Research Institute, "​Phosphate Backbone." https://www.genome.gov/genetics-glossary/Phosphate-Backbone  

National Human Genome Research Institute, "What is the Human Genome Project?" https://www.genome.gov/human-genome-project/What  

‌ Live Science contributor Ashley Hamer updated this article on Jan. 16, 2022.

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How to Write up a Science Experiment

Last Updated: May 19, 2023 Fact Checked

This article was co-authored by Jessie Antonellis-John . Jessie Antonellis-John is a Math and Science Instructor who teaches at Southwestern Oregon Community College. With over 10 years of experience, she specializes in curriculum development. Jessie earned her PhD in Teaching & Teacher Education from the University of Arizona, her Master of Education from Western Governors University, and her BS in Astrophysics from Mount Holyoke College. She’s also co-authored several peer-reviewed journal articles in professional publications. This article has been fact-checked, ensuring the accuracy of any cited facts and confirming the authority of its sources. This article has been viewed 277,495 times.

Any time you have conducted a science experiment, you should write a lab report detailing why the experiment was performed, the results you expected, the process you used, the actual results, and a discussion of what the results mean. Lab reports often follow a very standard format starting with an abstract and introduction, followed by a materials and methods section, the results and discussion, and finally a conclusion. This format will allow the reader to find answers to common questions that are often asked: Why was the experiment performed? What were the expected results? How was the experiment conducted? What happened in the experiment? What do the results mean? This article explains the basic format of a lab report.

Lab Report Template

Writing an Abstract and Introduction

• The purpose of this short summary is to provide the reader with enough information on the experiment that they can see if they want or need to read the entire report. The abstract helps them determine if your research is relevant to them.
• Devote a sentence to describing the purpose of the project and its significance. Then, very briefly describe the materials and methods used. Follow up with a 1-2 sentence description of the results of the experiment. You might also provide a list of keywords listing subjects related to your research.

• The introduction will outline what the experiment is, why it was done, and why it is important. It must provide the reader with two key pieces of information: what is the question the experiment is supposed to answer and why is answering this question important.

• A research hypothesis should be a brief statement that pares down your problem that you described in your introduction into something that is testable and falsifiable.
• Scientists must create a hypothesis from which an experiment can reasonably be designed and carried out.
• A hypothesis is never proved in an experiment, only "verified" or "supported".

• For example, you might start with "Fertilizer affects how tall a plant will grow". You could expand this idea to a clear hypothesis: "Plants grow faster and taller when they are given fertilizer". To make it a testable hypothesis, you could add experimental details: "Plants which are given a solution with 1ml of fertilizer grow faster than plants without fertilizer because they are given more nutrients."

Explaining Your Research Procedure

• This section is extremely crucial documentation of your methods of analysis.

• For example, if you were testing how fertilizer affects plant growth, you would want to state what brand of fertilizer you used, what species of plant you used and what brand of seed.
• Make sure you include the quantity of all objects used in the experiment.

• Remember all experiments involve controls and variables. Describe these here.
• If you used a published laboratory method, be sure to provide a reference for the original method.

Reporting Results

• For example, if you are testing the effect of fertilizer on plant growth you would want a graph showing the average growth of plants given fertilizer vs. those without.
• You would also want to describe the result. For example "Plants which were given a concentration of 1ml of fertilizer grew an average of 4 cm taller than those that were not given fertilizer."
• As you go along, narrate your results. Tell the reader why a result is significant to the experiment or problem. This will allow the reader to follow your thinking process.
• Compare your results to your original hypothesis. State whether or not your hypothesis was supported or not by your experiment.
• Quantitative data is anything expressed in terms of numerical forms such as percentages or statistics. Qualitative data is derived from broad questions and is expressed in the form of word responses from study participants.

• In this section, the author can address other questions such as: "why did we get an unexpected result?" or "what would happen if one aspect of the procedure was altered?".
• If your results did not verify your hypothesis, explain your reasoning why.

• Be sure to link back to the introduction and whether or not the experiment addressed the goals of your analysis.

• You can use software such as EndNote to help you cite and build a properly referenced bibliography.

Expert Q&A

You Might Also Like

• ↑ Michael Simpson, PhD. Registered Professional Biologist. Expert Interview. 8 September 2021.
• ↑ https://www.matrix.edu.au/how-to-write-a-scientific-report/
• ↑ https://explorable.com/research-hypothesis
• ↑ https://www.monash.edu/learnhq/write-like-a-pro/annotated-assessment-samples/science/science-lab-report

About This Article

When you’re writing up a science experiment for a class, break it into sections for your introduction, procedure, findings, and conclusion. In the intro, explain the purpose of your experiment and what you predicted would happen, then give a brief overview of what you did. In the procedure section, describe all of the materials you used and give a step-by-step account of your method. In the findings section, give the results from your experiment, including any graphs or diagrams you made. Then, explain if your expectations were met and what further research you can do. Finish with a brief conclusion that summarizes your experiment and its results. For more tips from our Science co-author, including how to write an abstract for your science paper, read on! Did this summary help you? Yes No

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Methodology

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

Guide to Experimental Design | Overview, 5 steps & Examples

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

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

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

There are five key steps in designing an experiment:

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

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

Table of contents

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

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

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

Start by simply listing the independent and dependent variables .

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Randomization

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

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

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

Between-subjects vs. within-subjects

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

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

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

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

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

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

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

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

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

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

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

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

Research bias

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

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

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

When designing the experiment, you decide:

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

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

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

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

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

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

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

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

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

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

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• Open access
• Published: 29 October 2009

The philosophy of scientific experimentation: a review

• Hans Radder 1 , 2  

Automated Experimentation volume  1 , Article number:  2 ( 2009 ) Cite this article

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Practicing and studying automated experimentation may benefit from philosophical reflection on experimental science in general. This paper reviews the relevant literature and discusses central issues in the philosophy of scientific experimentation. The first two sections present brief accounts of the rise of experimental science and of its philosophical study. The next sections discuss three central issues of scientific experimentation: the scientific and philosophical significance of intervention and production, the relationship between experimental science and technology, and the interactions between experimental and theoretical work. The concluding section identifies three issues for further research: the role of computing and, more specifically, automating, in experimental research, the nature of experimentation in the social and human sciences, and the significance of normative, including ethical, problems in experimental science.

The rise of experimental science

Over the past decades the historical development of experimental science has been studied in detail. One focus has been on the nature and role of experiment during the rise of the natural sciences in the sixteenth and seventeenth centuries. Earlier accounts of this so-called Scientific Revolution emphasized the universalization of the mathematical method or the mechanization of the world-view as the decisive achievement. In contrast, the more recent studies of sixteenth and seventeenth century science stress the great significance of a new experimental practice and a new experimental knowledge. Major figures were Francis Bacon, Galileo Galilei, and Robert Boyle. The story of the controversy of the latter with Thomas Hobbes, during the late 1650s and early 1660s, has become a paradigm of the recent historiography of scientific experimentation [ 1 ]. While Hobbes defended the 'old' axiomatic-deductive style of the geometric tradition, Boyle advocated the more modest acquisition of probable knowledge of experimental 'matters of fact'. Simultaneously at stake in this controversy were the technical details of Boyle's air-pump experiments, the epistemological justification of the experimental knowledge and the social legitimacy of the new experimental style of doing science.

A more wide-ranging account of the role of experimentation in the natural sciences has been proposed by Thomas Kuhn [ 2 ]. He claims that the rise of modern physical science resulted from two simultaneous developments. On the one hand, a radical conceptual and world-view change occurred in what he calls the classical, or mathematical, sciences, such as astronomy, statics and optics. On the other, the novel type of Baconian, or experimental, sciences emerged, dealing with the study of light, heat, magnetism and electricity, among other things. Kuhn argues that it was not before the second half of the nineteenth century that a systematic interaction and merging of the experimental and mathematical traditions took place. An example is the transformation of the Baconian science of heat into an experimental-mathematical thermodynamics during the first half of the nineteenth century. At about the same time, the interactions between (at first, mainly experimental) science and technology increased substantially. Important results of this scientification of technology were chemical dye stuffs and artificial fertilizers.

Starting in the second half of the nineteenth century, extensive experimentation also took root in various other sciences. This happened in medicine, in particular in physiology, somewhat later in psychology, and still later in the social sciences. A characteristic feature of many experiments in those sciences is a strong reliance on statistical methods (see, e.g., [ 3 ]).

The rise of the philosophy of scientific experimentation

Alongside the actual practices of experimentation, a variety of authors--both philosophers and philosophy-minded scientists--have reflected upon the nature and function of scientific experiments. Among the better-known examples are Bacon's and Galileo's advocacy of the experimental method. John Stuart Mill (around the middle of the nineteenth century) and Ernst Mach (late nineteenth-early twentieth century) provided some methodological and epistemological analyses of experimentation. Claude Bernard promoted and analyzed the use of the experimental method in medicine. His Introduction to the Study of Experimental Medicine [ 4 ] influenced a number of twentieth century French writers, including Pierre Duhem, Gaston Bachelard and Georges Canguilhem. While those authors addressed some aspects of experimentation in their accounts of science, a substantial and coherent tradition in the philosophy of scientific experimentation did not yet arise.

Such a tradition did spring up in Germany, in the second half of the twentieth century. Within this German tradition two approaches may be distinguished. One developed Hugo Dingler's pioneering work [ 5 ]. Dingler emphasized the manipulation and intervention character of experimentation, and hence its kinship to technology. One of his aims was to show how the basic theoretical concepts of physics, such as length or mass, could be grounded in concrete experimental actions. During the 1960s and 1970s, this part of Dingler's views was taken up and systematically developed by several other German philosophers, including Paul Lorenzen, Klaus Holzkamp and Peter Janich. More recently, the emphasis on the methodical construction of theoretical concepts in terms of experimental actions has given way to a more culturalistic interpretation of experimental procedures and results [ 6 ].

A second approach within the German tradition took its departure even more directly from the kinship between experiment and technology. The major figure here is the early Jürgen Habermas. In his work from the 1960s, Habermas conceived of (empirical-analytical) science as 'anticipated technology', the crucial link being experimental action [ 7 ]. In the spirit of Karl Marx, Martin Heidegger and Herbert Marcuse, Habermas' aim was not merely to develop a theory of (scientific) knowledge but rather a critique of technocratic reason. More recently, attempts have been made to connect this German tradition to Anglo-Saxon philosophy of experiment [ 8 , 9 ] and to contemporary social studies of science and technology [ 10 ]. Recent work on 'science as technology' by Srđan Lelas [ 11 ] can be characterized as, broadly, inspired by this second branch of the German tradition.

In the English-speaking world, a substantial number of studies of scientific experimentation have been written since the mid-1970s. They resulted from the Kuhnian 'programs in history and philosophy of science'. In their studies of (historical or contemporary) scientific controversies, sociologists of scientific knowledge often focused on experimental work (e.g., [ 12 ]), while so-called laboratory studies addressed the ordinary practices of experimental scientists (e.g., [ 13 ]). An approach that remained more faithful to the history and philosophy of science idea started with Ian Hacking's argument for the relative autonomy of experimentation and his plea for a philosophical study of experiment as a topic in its own right [ 14 ]. It includes work by Allan Franklin, Peter Galison, David Gooding and Hans-Jörg Rheinberger, among many others (see the edited volumes [ 15 , 16 ] and [ 17 ]).

More recently, several philosophers argue that a further step should be taken by combining the results of the historical and sociological study of experiment with more developed theoretical-philosophical analyses [ 18 ]. A mature philosophy of experiment, they claim, should not be limited to summing up its practical features but attempt to provide a systematic analysis of experimental practice and experimental knowledge. The latter is often lacking in the sociological and historical literature on scientific experimentation.

Intervention and production, and their philosophical implications

Looking at the specific features of experiments within the overall practice of science, there is one feature that stands out. In order to perform experiments, whether they are large-scale or small-scale, experimenters have to intervene actively in the material world; moreover, in doing so they produce all kinds of new objects, substances, phenomena and processes. More precisely, experimentation involves the material realization of the experimental system (that is to say, the object(s) of study, the apparatus, and their interaction) as well as an active intervention in the environment of this system. In this respect, experiment contrasts with theory even if theoretical work is always attended with material acts (such as the typing or writing down of a mathematical formula). Hence, a central issue for a philosophy of experiment is the question of the nature of experimental intervention and production, and their philosophical implications. To be sure, at times scientists devise and discuss so-called thought experiments [ 19 ]. However, such 'experiments'--in which the crucial aspect of intervention and production is missing--are better conceived as not being experiments at all but rather as particular types of theoretical argument, which may or may not be materially realizable in experimental practice.

Clearly, not just any kind of intervention in the material world counts as a scientific experiment. Quite generally, one may say that successful experiments require, at least, a certain stability and reproducibility, and meeting this requirement presupposes a measure of control of the experimental system and its environment as well as a measure of discipline of the experimenters and the other people involved in realizing the experiment.

Experimenters employ a variety of strategies for producing stable and reproducible experiments (see, e.g., [ 20 , 21 ] and [ 6 ]). One such strategy is to attempt to realize 'pure cases' of experimental effects. For example, in some early electromagnetic experiments carried out in the 1820s, André Ampère investigated the interaction between an electric current and a freely suspended magnetic needle [ 22 ]. He systematically varied a number of factors of his experimental system and examined whether or not they were relevant, that is to say, whether or not they had a destabilizing impact on the experimental process.

Furthermore, realizing a stable object-apparatus system requires knowledge and control of the (actual and potential) interactions between this system and its environment. Depending on the aim and design of the experiment, specific interactions may be necessary (and hence required), allowed (but irrelevant), or forbidden (because disturbing). Thus, in his experiments on electromagnetism, Ampère anticipated a potential disturbance exerted by the magnetism of the earth. In response, he designed his experiment in such a way that terrestrial magnetism constituted an allowed rather than a forbidden interaction.

A further aspect of experimental stability is implied by the notion of reproducibility [ 9 ]. A successful performance of an experiment by the original experimenter is an achievement that may depend on certain idiosyncratic aspects of a local situation. Yet, a purely local experiment that cannot be carried out in other experimental contexts will, in the end, be unproductive for science. However, since the performance of an experiment is a complex process, no repetition will be strictly identical to the original experiment and many repetitions may be dissimilar in several respects. For this reason, we need to specify what we take or require to be reproducible (for instance, a particular aspect of the experimental process or a certain average over different runs). Furthermore, there is the question of who should be able to reproduce the experiment (for instance, the original experimenter, contemporary scientists, or even any scientist or human being). Investigating these questions leads to different types and ranges of experimental reproducibility, which can be observed to play different roles in experimental practice.

Laboratory experiments in physics, chemistry and molecular biology often allow one to control the objects under investigation to such an extent that the relevant objects in successive experiments may be assumed to be in identical states. Hence, statistical methods are employed primarily to further analyze or process the data (see, for instance, the error-statistical approach by Deborah Mayo [ 23 ]). In contrast, in field biology, medicine, psychology and social science, such a strict experimental control is often not feasible. To compensate for this, statistical methods in these areas are used directly to construct groups of experimental subjects that are presumed to possess identical average characteristics. It is only after such groups have been constructed that one can start the investigation of hypotheses about the research subjects. One can phrase this contrast in a different way by saying that in the former group of sciences statistical considerations mostly bear upon linking experimental data and theoretical hypotheses, while in the latter group it is often the case that statistics already play a role at the stage of producing the actual individual data.

The intervention and production aspect of scientific experimentation carries implications for several philosophical questions. A general lesson, already drawn by Bachelard, appears to be this: the intervention and production character of experimentation entails that the actual objects and phenomena themselves are, at least in part, materially realized through human interference. Hence, it is not just the knowledge of experimental objects and phenomena but also their actual existence and occurrence that prove to be dependent on specific, productive interventions by the experimenters. This fact gives rise to a number of important philosophical issues. If experimental objects and phenomena have to be realized through active human intervention, does it still make sense to speak of a 'natural' nature or does one merely deal with artificially produced laboratory worlds? If one does not want to endorse a fully-fledged constructivism, according to which the experimental objects and phenomena are nothing but artificial, human creations, one needs to develop a more differentiated categorization of reality. In this spirit, various authors (e.g., [ 20 , 9 ]) have argued that an appropriate interpretation of experimental science needs some kind of dispositional concepts, such as powers, potentialities, or tendencies. These human-independent dispositions would then underlie and enable the human construction of particular experimental processes.

A further important question is whether scientists, on the basis of artificial experimental intervention, can acquire knowledge of a human-independent nature. Some philosophers claim that, at least in a number of philosophically significant cases, such 'back inferences' from the artificial laboratory experiments to their natural counterparts can be justified. Another approach accepts the constructed nature of much experimental science, but stresses the fact that its results acquire a certain endurance and autonomy with respect to both the context in which they have been realized in the first place and later developments. In this vein, Davis Baird [ 24 ] offers an account of 'objective thing knowledge', the knowledge encapsulated in material things, such as Watson and Crick's material double helix model or the Indicator of Watt and Southern's steam engine.

Another relevant feature of experimental science is the distinction between the working of an apparatus and its theoretical accounts. In actual practice it is often the case that experimental devices work well, even if scientists disagree on how they work. This fact supports the claim that variety and variability at the theoretical level may well go together with a considerable stability at the level of the material realization of experiments. This claim can then be exploited for philosophical purposes, for example to vindicate entity realism [ 14 ] or referential realism [ 8 ].

The relationship between (experimental) science and technology

Traditionally, philosophers of science have defined the aim of science as, roughly, the generation of reliable knowledge of the world. Moreover, as a consequence of explicit or implicit empiricist influences, there has been a strong tendency to take the production of experimental knowledge for granted and to focus on theoretical knowledge. However, if one takes a more empirical look at the sciences, both at their historical development and at their current condition, this approach must be qualified as one-sided. After all, from Archimedes' lever-and-pulley systems to the cloned sheep Dolly, the development of (experimental) science has been intricately interwoven with the development of technology ([ 25 , 26 ]). Experiments make essential use of (often specifically designed) technological devices, and, conversely, experimental research often contributes to technological innovations. Moreover, there are substantial conceptual similarities between the realization of experimental and that of technological processes, most significantly the implied possibility and necessity of the manipulation and control of nature. Taken together, these facts justify the claim that the science-technology relationship ought to be a central topic for the study of scientific experimentation.

One obvious way to study the role of technology in science is to focus on the instruments and equipment employed in experimental practice. Many studies have shown that the investigation of scientific instruments is a rich source of insights for a philosophy of scientific experimentation (see, e.g. [ 15 , 17 , 18 ] and [ 27 ]). One may, for example, focus on the role of visual images in experimental design and explore the wider problem of the relationship between thought and vision. Or one may investigate the problem of how the cognitive function of an intended experiment can be materially realized, and what this implies for the relationship between technological functions and material structures. Or one may study the modes of representation of instrumentally mediated experimental outcomes and discuss the question of the epistemic or social appraisal of qualitative versus quantitative results.

In addition to such studies, several authors have proposed classifications of scientific instruments or apparatus. One suggested distinction is that between instruments that represent a property by measuring its value (e.g., a device that registers blood pressure), instruments that create phenomena that do not exist in nature (e.g., a laser), and instruments that closely imitate natural processes in the laboratory (e.g., an Atwood machine, which mimics processes and properties of falling objects).

Such classifications form an excellent starting point for investigating further philosophical questions on the nature and function of scientific instrumentation. They demonstrate, for example, the inadequacy of the empiricist view of instruments as mere enhancers of human sensory capacities. Yet, an exclusive focus on the instruments as such may tend to ignore two things. First, an experimental setup often includes various 'devices', such as a concrete wall to shield off dangerous radiation, a support to hold a thermometer, a spoon to stir a liquid, curtains to darken a room, and so on. Such devices are usually not called instruments, but they are equally crucial to a successful performance and interpretation of the experiment and hence should be taken into account. Second, a strong emphasis on instruments may lead to a neglect of the environment of the experimental system, especially of the requirement to control the interactions between the experimental system and its environment. Thus, a comprehensive view of scientific experimentation needs to go beyond an analysis of the instrument as such by taking full account of the specific setting in which this instrument needs to function.

Finally, there is the issue of the general philosophical significance of the experiment-technology relationship. Some of the philosophers who emphasize the importance of technology for science endorse a 'science-as-technology' account. That is to say, they advocate an overall interpretation in which the nature of science--not just experimental but also theoretical science--is seen as basically or primarily technological (see for instance, [ 5 , 7 ] and [ 11 ]). Other authors, however, take a less radical view by criticizing the implied reduction of science to technology and by arguing for the sui generis character of theoretical-conceptual and formal-mathematical work. Thus, while stressing the significance of the technological--or perhaps, more precisely, the intervention and production dimension of science--these views nevertheless see this dimension as complementary to a theoretical dimension (see, e.g., [ 8 , 24 ] and [ 28 ]).

The role of theory in experimentation

This brings us to a further central theme in the study of scientific experimentation, namely the relationship between experiment and theory. The theme can be approached in two ways. One approach addresses the question of how theories or theoretical knowledge may arise from experimental practices. Thus, Franklin [ 21 ] has provided detailed descriptions and analyses of experimental confirmations and refutations of theories in twentieth century physics. Giora Hon [ 28 ] has put forward a classification of experimental error, and has argued that the notion of error may be exploited to elucidate the transition from the material, experimental processes to propositional, theoretical knowledge (see also [ 29 ]).

A second approach to the experiment-theory relationship examines the question of the role of existing theories, or theoretical knowledge, within experimental practices. Over the last 25 years, this question has been debated in detail. Are experiments, factually or logically, dependent on prior theories, and if so, in which respects and to what extent? The remainder of this section reviews some of the debates on this question.

The strongest version of the claim that experimentation is theory dependent says that all experiments are planned, designed, performed, and used from the perspective of one or more theories about the objects under investigation. In this spirit, Justus von Liebig and Karl Popper, among others, advocated the view that all experiments are explicit tests of existing theories. This view completely subordinates experimental research to theoretical inquiry. However, on the basis of many studies of experimentation published during the last 25 years, it can be safely concluded that this claim is false. For one thing, quite frequently the aim of experiments is just to realize a stable phenomenon or a working device. Yet, the fact that experimentation involves much more than theory testing does not, of course, mean that testing a theory may not be an important goal in particular scientific settings.

At the other extreme, there is the claim that, basically, experimentation is theory-free. The older German school of 'methodical constructivism' (see [ 6 ]) came close to this position. A somewhat more moderate view is that, in important cases, theory-free experiments are possible and do occur in scientific practice. This view admits that performing such 'exploratory' experiments does require some ideas about nature and apparatus, but not a well-developed theory about the phenomena under scrutiny. Ian Hacking [ 14 ] and Friedrich Steinle [ 22 ] make this claim primarily on the basis of case studies from the history of experimental science. Michael Heidelberger [ 30 ] aims at a more systematic underpinning of this view. He distinguishes between theory-laden and causally-based instruments and claims that experiments employing the latter type of instruments are basically theory-free.

Another view admits that not all concrete activities that can be observed in scientific practice are guided by theories. Yet, according to this view, if certain activities are to count as a genuine experiment, they require a theoretical interpretation (see [ 8 , 9 , 28 ] and [ 31 ]). More specifically, performing and understanding an experiment depends on a theoretical interpretation of what happens in materially realizing the experimental process. In general, quite different kinds of theory may be involved, such as general background theories, theories or theoretical models of the (material, mathematical, or computational) instruments, and theories or theoretical models of the phenomena under investigation.

One argument for such claims derives from the fact that an experiment aims to realize a reproducible correlation between an observable feature of the apparatus and a feature of the object under investigation. The point is that materially realizing this correlation and knowing what can be learned about the object from inspecting the apparatus depends on theoretical insights about the experimental system and its environment. Thus, these insights pertain to those aspects of the experiment that are relevant to obtaining a reproducible correlation. It is not necessary, and in practice it will usually not be the case, that the theoretical interpretation offers a full understanding of any detail of the experimental process.

A further argument for the significance of theory in experimentation notes that a single experimental run is not enough to establish a stable result. A set of different runs, however, will almost always produce values that are, more or less, variable. The questions then are: What does this fact tell us about the nature of the property that has been measured? Does the property vary within the fixed interval? Is it a probabilistic property? Or is its real value constant and are the variations due to random fluctuations? In experimental practice, answers to such questions are based on an antecedent theoretical interpretation of the nature of the property that has been measured.

Regarding these claims, it is important to note that, in actual practice, the theoretical interpretation of an experiment will not always be explicit and the experimenters will not always be aware of its use and significance. Once the performance of a particular experiment or experimental procedure becomes routine, the theoretical assumptions drop out of sight: they become like an (invisible) 'window to the world'. Yet, in a context of learning to perform and understand the experiment or in a situation where its result is very consequential or controversial, the implicit interpretation will be made explicit and subjected to empirical and theoretical scrutiny. This means that the primary locus of the theoretical interpretation is the relevant scientific community and not the individual experimenter.

In conclusion: further issues in scientific experimentation

As we have seen, the systematic philosophical study of scientific experimentation is a relatively recent phenomenon. Hence, there are a number of further issues that have received some attention but merit a much more detailed account. In concluding this review paper, three such issues will be briefly discussed.

First, recent scientific practice shows an ever-increasing use of 'computer experiments'. These involve various sorts of hybrids of material intervention, computer simulation, and theoretical and mathematical modeling techniques (see [ 32 ]). Often, more traditional experimental approaches are challenged and replaced by approaches resting fully or primarily on computer simulations (sometimes this replacement is based on budgetary considerations only). More generally, there is a large variety of uses of computer science and technology in performing, analyzing and interpreting experiments and in visualizing, storing and disseminating their results. Automated experimentation constitutes a significant part of these developments.

These new developments raise important questions for the scholarly study of scientific experimentation. First, although some pioneering work has been done (see, for instance, [ 33 ] about the role of databases, and, more generally, bioinformatics in research in the life sciences), we need many more empirical studies that chart this new terrain. Furthermore, new methodological questions arise about how to do this automated experimentation in innovative, yet plausible, ways. As the history of Artificial Intelligence teaches us, expectations about automation can sometimes be overenthusiastic and unfounded ([ 34 , 35 ]). For this reason, a critical assessment of what can, and what cannot, be achieved through automation is particularly important (for the cases of formal symbol manipulation and neural network approaches to AI, see [ 36 ], chaps. 5 and 12). Related to this is the epistemological question of the justifiability of the results of the new approaches. Should experiments always involve a substantial material component or are simulated experiments equally reliable and useful (see [ 37 ])? Finally, computer experiments are regularly applied to complex and large-scale systems, for instance in climate science. Often, in such contexts, scientific and policy problems are intimately connected. This connection also constitutes an important topic for the study of scientific experimentation (see, e.g., [ 38 ]).

A second issue that merits more attention is the nature and role of experimentation in the social and human sciences, such as economics, sociology, medicine, and psychology. Practitioners of those sciences often label substantial, or even large, parts of their activities as 'experimental'. So far, this fact is not reflected in the philosophical literature on experimentation, which has primarily focused on the natural sciences. Thus, a challenge for future research is to connect the primarily methodological literature on experimenting in economics, sociology, medicine, and psychology with the philosophy of science literature on experimentation in natural science (see, e.g., [ 39 ] and [ 40 ]).

One subject that will naturally arise in philosophical reflection upon the similarities and dissimilarities of natural and social or human sciences is this: In experiments on human beings, the experimental subjects will often have their own interpretation of what is going on in these trials, and this interpretation may influence their responses over and above the behavior intended by the experimenters. As a methodological problem (of how to avoid 'biased' responses) this is of course well known to practitioners of the human and social sciences. However, from a broader philosophical or socio-cultural perspective the problem is not necessarily one of bias. It may also reflect a clash between a scientific and a common-sense interpretation of human beings. In case of such a clash, social and ethical issues are at stake, since the basic question is who is entitled to define the nature of human beings: the scientists or the people themselves? The methodological, ethical, and social issues springing from this question will continue to be a significant theme for the study of experimentation in the human and social sciences.

This brings us to a last issue. The older German tradition explicitly addressed wider normative questions surrounding experimental science and technology. The views of Habermas, for example, have had a big impact on broader conceptualizations of the position of science and technology in society. Thus far, the more recent Anglophone approaches within the philosophy of scientific experimentation have primarily dealt with more narrowly circumscribed scholarly topics. In so far as normative questions have been taken into account, they have been mostly limited to epistemic normativity, for instance to questions of the proper functioning of instruments or the justification of experimental evidence. Questions regarding the connections between epistemic and social or ethical normativity are hardly addressed.

Yet, posing such questions is not far-fetched. For instance, those experiments that use animals or humans as experimental subjects are confronted with a variety of normative issues, often in the form of a tension between methodological and ethical requirements [ 41 ]. Other normatively relevant questions relate to the issue of the artificial and the natural in experimental science and science-based technology. Consider, for example, the question of whether experimentally isolated genes are natural or artificial entities. This question is often discussed in environmental philosophy, and different answers to it entail a different environmental ethics and politics. More specifically, the issue of the contrast between the artificial and the natural is crucial to debates about patenting, in particular the patenting of genes and other parts of organisms. The reason is that discoveries of natural phenomena are not patentable while inventions of artificial phenomena are [ 42 ].

Although philosophers of experiment cannot be expected to solve all of those broader social and normative problems, they may be legitimately asked to contribute to the debate on possible approaches and solutions. In this respect, the philosophy of scientific experimentation could profit from its kinship to the philosophy of technology, which has always shown a keen sensitivity to the interconnectedness between technological and social or normative issues.

Shapin S, Schaffer S: Leviathan and the Air-Pump: Hobbes, Boyle, and the Experimental Life. 1985, Princeton: Princeton University Press

Google Scholar  

Kuhn TS: Mathematical versus experimental traditions in the development of physical science. The Essential Tension. 1977, Chicago: University of Chicago Press, 31-65.

Campbell DT, Stanley JC: Experimental and Quasi-Experimental Designs for Research. 1963, Chicago: Rand McNally College Publishing Company

Bernard C: An Introduction to the Study of Experimental Medicine. 1957, New York: Dover Publications

Dingler H: Das Experiment. Sein Wesen und seine Geschichte. 1928, München: Verlag Ernst Reinhardt

Janich P: Konstruktivismus und Naturerkenntnis. 1996, Frankfurt am Main: Suhrkamp

Habermas J: Knowledge and Human Interests. 1978, London: Heinemann, 2

Radder H: The Material Realization of Science. 1988, Assen: Van Gorcum

Radder H: In and about the World. 1996, Albany: State University of New York Press

Feenberg A: Questioning Technology. 1999, London: Routledge

Lelas S: Science and Modernity: Toward an Integral Theory of Science. 2000, Dordrecht: Kluwer

Book   Google Scholar  

Collins HM: Changing Order: Replication and Induction in Scientific Practice. 1985, London: Sage

Latour B, Woolgar S: Laboratory Life: The Social Construction of Scientific Facts. 1979, London: Sage

Hacking I: Representing and Intervening. 1983, Cambridge: Cambridge University Press

Gooding D, Pinch T, Schaffer S, Eds: The Uses of Experiment. 1989, Cambridge: Cambridge University Press

Buchwald JZ, Ed: Scientific Practice: Theories and Stories of Doing Physics. 1995, Chicago: University of Chicago Press

Heidelberger M, Steinle F, Eds: Experimental Essays--Versuche zum Experiment. 1998, Baden-Baden: Nomos Verlagsgesellschaft

Radder H, Ed: The Philosophy of Scientific Experimentation. 2003, Pittsburgh: University of Pittsburgh Press

Brown JR: The Laboratory of the Mind: Thought Experiments in the Natural Sciences. 1991, London: Routledge

Bhaskar R: A Realist Theory of Science. 1978, Hassocks: Harvester Press

Franklin A: The Neglect of Experiment. 1986, Cambridge: Cambridge University Press

Steinle F: Exploratives vs. theoriebestimmtes Experimentieren: Ampères erste Arbeiten zum Elektromagnetismus. In [17];. 1998, 272-297.

Mayo DG: Error and the Growth of Experimental Knowledge. 1996, Chicago: University of Chicago Press

Baird D: Thing Knowledge: A Philosophy of Scientific Instruments. 2004, Berkeley: University of California Press

Tiles M, Oberdiek H: Living in a Technological Culture. 1995, London: Routledge

Radder H: Science, technology and the science-technology relationship. Philosophy of Technology and Engineering Sciences. Edited by: Meijers AWM. 2009, Amsterdam: Elsevier, 65-91.

Chapter   Google Scholar  

Rothbart D: Philosophical Instruments. Minds and Tools at Work. 2007, Urbana: University of Illinois Press

Hon G: The idols of experiment: transcending the 'etc. list'. In [18];. 2003, 174-197.

Hon G, Schickore J, Steinle F, Eds: Going Amiss in Experimental Research. 2009, New York: Springer

Heidelberger M: Theory-ladenness and scientific instruments in experimentation. In [18];. 2003, 138-151.

Morrison M: Theory, intervention and realism. Synthese. 1990, 82: 1-22. 10.1007/BF00413667.

Article   Google Scholar  

Lenhard J, Küppers G, Shinn T, Eds: Simulation: Pragmatic Constructions of Reality. 2007, Dordrecht: Springer

Leonelli S: Packaging data for re-use: databases in model organism biology. How Well Do 'Facts' Travel?. Edited by: Howlett P, Morgan MS. Cambridge: Cambridge University Press,

Dreyfus HL: What Computers Still Can't Do. 1992, Cambridge, MA: MIT Press

Collins HM: Artificial Experts: Social Knowledge and Intelligent Machines. 1990, Cambridge, MA: MIT Press

Radder H: The World Observed/The World Conceived. 2006, Pittsburgh: University of Pittsburgh Press

Morgan MS: Experiments without material intervention. Model experiments, virtual experiments, and virtually experiments. In [18];. 2003, 216-235.

Petersen AC: Simulating Nature. A Philosophical Study of Computer-Simulation Uncertainties and their Role in Climate Science and Policy Advice. 2006, Apeldoorn: Het Spinhuis

Winston AS, Blais DJ: What counts as an experiment?: A transdisciplinary analysis of textbooks, 1930-1970. American Journal of Psychology. 1996, 109: 599-616. 10.2307/1423397.

Guala F: The Methodology of Experimental Economics. 2005, Cambridge: Cambridge University Press

Resnik DB: The Ethics of Science. 1998, London: Routledge

Sterckx S, Ed: Biotechnology, Patents and Morality. 2000, Aldershot: Ashgate, 2

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This article draws on material from an earlier publication. Copyright ( © 2006) From The Philosophy of Science. An Encyclopedia , edited by Sahotra Sarkar and Jessica Pfeifer. Reproduced by permission of Taylor and Francis Group, LLC, a division of Informa plc.

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howtoscience.net

A de-mystifying guide to the scientific writing process.

All scientific papers need an aim or series of aims. Essentially, the aims describe why you carried out this particular set of experiments. So if you don’t have one, your paper doesn’t have a purpose. The aim can be presented at the end of the introduction, or in a separate section afterward (imaginatively) titled “Aims”. How it’s presented in a paper is generally down to what journal style you’re following.

So what makes an aim? In a word, specificity. Aims should not be general, or “wishy washy”. I’ve seen a lot of aims written by students that say things like “to learn about [topic x]…” This is to be avoided for several reasons. Firstly, imagine this from the readers’ point of view: they’re reading this paper to find out about a new piece of research, they’re not interested in your learning process, they want to see results.

Secondly, the general nature of that statement means that there are myriad different ways we could satisfy the condition and make it true. You could learn about the topic by reading a textbook, or watching someone else perform an experiment, or go to a university lecture on the subject. But that’s not what you’re reporting in your paper. So, you must be as specific as you can.

The structure of an aim statement will often take on the form: “to use [method x] to test [condition y].” The addition of the method makes your aim much more specific, as different methodologies may alter the outcome and therefore what conclusions you can draw. It also reflects what you’re actually reporting in your paper. The condition you’re testing should test some aspect of your hypothesis and also be as specific as possible.

You can see in this example that one of the aims from this paper is following a very similar form to that described above. As usual, the jargon is highlighted in blue. If we remove the jargon and replace it with place-holder text, we get the form: "To expand understanding of the mechanisms of [the phenomenon we're studying] we [performed certain experiments] and then compared them to [previous data]". Howden et al. , (2011)

In the above example, the general term “expand understanding” goes against what I’ve said previously about being specific. However, if you read the paper, you’ll see that the researchers were in the dark a little as to what was going on and didn’t have an obvious candidate to study. Instead they went searching for the thing responsible for the phenomenon they were interested in. Even so, they have included methodology in their aim statement, making it as specific as possible, given their project goals and the knowledge they had at the time.

This kind of “blue sky” open ended research obviously has to have more general aims because you’re never quite sure what you’re going to find - and sometimes you don’t know what you’re looking for. This contrasts with “directed research” which has specific objectives in mind. These could be things like “does [chemical compound x] kill cancer tumours?” or “do bowling balls and feathers actually fall at the same speed without air resistance?”

To summarise, think carefully about the objectives of the experiments in your report and ask yourself what they were designed to show. Condense these down to simple, testable statements and you have the aims.

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Understanding Science

How science REALLY works...

• Understanding Science 101
• Misconceptions
• Testing ideas with evidence from the natural world is at the core of science.
• Scientific testing involves figuring out what we would  expect  to observe if an idea were correct and comparing that expectation to what we  actually  observe.
• Scientific arguments are built from an idea and the evidence relevant to that idea.
• Scientific arguments can be built in any order. Sometimes a scientific idea precedes any evidence relevant to it, and other times the evidence helps inspire the idea.

Misconception:  Science proves ideas.

Misconception:  Science can only disprove ideas.

Correction:  Science neither proves nor disproves. It accepts or rejects ideas based on supporting and refuting evidence, but may revise those conclusions if warranted by new evidence or perspectives.  Read more about it.

The core of science: Relating evidence and ideas

In this case, the term  argument  refers not to a disagreement between two people, but to an evidence-based line of reasoning — so scientific arguments are more like the closing argument in a court case (a logical description of what we think and why we think it) than they are like the fights you may have had with siblings. Scientific arguments involve three components: the idea (a  hypothesis  or theory), the  expectations  generated by that idea (frequently called predictions), and the actual observations relevant to those expectations (the evidence). These components are always related in the same logical way:

• What would we expect to see if this idea were true (i.e., what is our expected observation)?
• What do we actually observe?
• Do our expectations match our observations?

PREDICTIONS OR EXPECTATIONS?

When scientists describe their arguments, they frequently talk about their expectations in terms of what a hypothesis or theory predicts: “If it were the case that smoking causes lung cancer, then we’d  predict  that countries with higher rates of smoking would have higher rates of lung cancer.” At first, it might seem confusing to talk about a prediction that doesn’t deal with the future, but that refers to something going on right now or that may have already happened. In fact, this is just another way of discussing the expectations that the hypothesis or theory generates. So when a scientist talks about the  predicted  rates of lung cancer, he or she really means something like “the rates that we’d expect to see if our hypothesis were correct.”

If the idea generates expectations that hold true (are actually observed), then the idea is more likely to be accurate. If the idea generates expectations that don’t hold true (are not observed), then we are less likely to  accept  the idea. For example, consider the idea that cells are the building blocks of life. If that idea were true, we’d expect to see cells in all kinds of living tissues observed under a microscope — that’s our expected observation. In fact, we do observe this (our actual observation), so evidence supports the idea that living things are built from cells.

Though the structure of this argument is consistent (hypothesis, then expectation, then actual observation), its pieces may be assembled in different orders. For example, the first observations of cells were made in the 1600s, but cell theory was not postulated until 200 years later — so in this case, the evidence actually helped inspire the idea. Whether the idea comes first or the evidence comes first, the logic relating them remains the same.

Here, we’ll explore scientific arguments and how to build them. You can investigate:

Putting the pieces together: The hard work of building arguments

• Predicting the past
• Arguments with legs to stand on

Or just click the  Next  button to dive right in!

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Scientific arguments rely on testable ideas. To learn what makes an idea testable, review our  Science Checklist .

• Forming hypotheses — scientific explanations — can be difficult for students. It is often easier for students to generate an expectation (what they think will happen or what they expect to observe) based on prior experience than to formulate a potential explanation for that phenomena. You can help students go beyond expectations to generate real, explanatory hypotheses by providing sentence stems for them to fill in: “I expect to observe A because B.” Once students have filled in this sentence you can explain that B is a hypothesis and A is the expectation generated by that hypothesis.
• You can help students learn to distinguish between hypotheses and the expectations generated by them by regularly asking students to analyze lecture material, text, or video. Students should try to figure out which aspects of the content were hypotheses and which were expectations.

Summing up the process

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Science in School

Simple gravimetric chemical analysis – weighing molecules the microscale way teach article.

Author(s): Bob Worley and Adrian Allan

Learn how to do quantitative chemistry using microscale techniques with bottle tops and inexpensive spirit burners that are relatively easy and quick to set up.

Quantitative chemistry using gravimetric analysis gives students the opportunity to experience chemical reactions, observe chemical changes, and use measurements of masses to determine the formula of a compound. This can be done using a combustion reaction, which results in a gain of mass (such as the reaction of magnesium with oxygen), or removing the water from a hydrated salt by heating, which results in a loss of mass. [ 1 ] These microscale practical activities are relatively simple and quick to do and can help students focus on the chemistry and reduce the load on working memory. Despite the small masses involved, the data generated from microscale experiments shows equivalent or better results than those obtained with traditional equipment, although a comparison of techniques is a useful exercise in error analysis. The advent of inexpensive, robust digital balances, measuring accurately to 0.01 g, has also allowed these methods to be more accessible and affordable than before.

Activity 1: Determining the formula of magnesium oxide

The determination of the formula of magnesium oxide by combustion of magnesium can yield variable results. Porcelain crucibles can be costly and can break during the experiment, and magnesium can escape when the lid is lifted. This product loss can reduce the accuracy of the result.

The microscale method uses an inexpensive alternative to expensive crucibles. The natural design of bottle tops allows a good flow of air with minimal loss of product.

This activity will take about 30 minutes and suitable for students aged 14–18.

• Magnesium ribbon, about 10–15 cm long (danger: flammable)
• Bunsen burner
• Two crown bottle tops (make sure the plastic coating has been removed from the bottle tops; this is easily done with a Bunsen burner and pair of tongs in a working fume cupboard)
• Mass balance
• Eye protection
• Nichrome wire, about 15 cm
• Small pipe-clay triangle
• Heating mat
• Activity 1 worksheet
• Find the total mass of two bottle tops and 15 cm of nichrome wire (M1) on a balance. Record the mass on the student worksheet.

• Roll a 10–15 cm length of magnesium ribbon around a pencil and place the ribbon on one of the bottle tops.
• Find the mass of the two bottle tops, nichrome wire, and magnesium ribbon (M2) and record on the worksheet.

• Set up a Bunsen burner and tripod on a heatproof mat. On the tripod, place a pipe-clay triangle small enough to support the bottle top ‘parcel’.
• Sandwich the magnesium between the two bottle tops (serrated edges together). Wrap the wire round the bottle tops to keep them together.
• Place the bottle tops securely on the pipe clay.

• Heat the bottle tops with a strong blue flame for 10 minutes.

• Switch off the Bunsen burner and allow the bottle tops to cool (for about 5 minutes).
• Find the mass of the bottle tops plus nichrome wire and magnesium oxide. Record this mass as M3.
• Use the masses of magnesium and magnesium oxide to calculate the number of moles of each substance. The molar ratio can be used to determine the formula of the compound.

Results and discussion

This activity can also be used with younger students who have not yet been taught mole calculations as way of introducing conservation of mass. Ask them to predict whether the mass of magnesium will get lighter, stay the same, or get heavier when heated, and test their prediction. Some will think the magnesium will get lighter, as they assume it will be ‘burnt away’ like carbon when it reacts with oxygen to form carbon dioxide. They are often surprised that oxygen atoms have mass, which can be measured on a balance after a combustion reaction with a metal.

A full explanation of the calculations can be found in the supporting material.

Sample result and calculation:

M1 = mass of bottle tops plus nichrome wire

M2 = mass of magnesium plus nichrome wire and magnesium

M3 = mass of the bottle top plus nichrome wire and magnesium oxide 

Mass of magnesium ribbon used (M2 − M1 = 4.11 − 3.87)  

Moles of magnesium = mass of Mg/gram formula mass of Mg = 0.24 ⁄ 24.5

Mass of oxygen used = M3 − M2 = 4.26 − 4.11

Moles of oxygen = mass of O/gram formula mass of O = 0.15 ⁄ 16

Ratio of magnesium to oxygen = moles Mg/moles O = 0.0098 ⁄ 0.0094  

The value should be close to one, giving a molar ratio of approximately one magnesium to one oxygen, which suggests the formula of magnesium oxide is indeed MgO.

Activity 2: Gravimetric determination of the formula of hydrated copper(II) sulfate

Gravimetric analysis to determine the moles of water present in a hydrated complex usually requires preweighing of a sample and heating to constant mass over a Bunsen burner using a crucible and a desiccator to prevent water from being reabsorbed from the air.

This method is quicker and uses a bottle top instead of a crucible, as described in Activity 1, along with spirit burners.

Spirit burners

Spirit burners burn cooler than Bunsen flames, which is advantageous for some experiments. A cheaper alternative to buying them from laboratory suppliers is to construct a homemade version made from small-scale jam jars.

A full guide on how to make a spirit burner is available in the supporting material. The assembly process can be observed in this video: https://www.youtube.com/watch?v=ndlycDnCM8c

The spirit burners can be used for other microscale practical applications, such as flame tests and determining the melting points of covalent molecular and ionic substances, [ 2 ] as well as for the cracking of hydrocarbons. [ 3 ]

In this experiment, the use of a spirit burner limits the extent to which copper sulfate will decompose to release toxic sulfur dioxide:

CuSO 4 ·5H 2 O(s) (pale-blue solid) ⇌ CuSO 4 (s) (white solid) + 5H 2 O(s)

Copper(II) sulfate pentahydrate (CuSO 4 ·5H 2 O) loses four of its water molecules at about 100 °C. The final water molecule is lost at 150 °C. With a Bunsen flame, a temperature of over 650 °C is reached, which causes the hydrated copper sulfate to decompose; the solid darkens and toxic sulfur dioxide and trioxide gases are released. As well as being hazardous, the decomposition affects the accuracy of the results. Using a cooler flame produced by a spirit burner prevents this decomposition.

Safety note

Wear eye protection.

• Bottle tops with plastic removed, as described in Activity 1
• Spirit burner (details of how to make one are given in the supporting material)
• Copper(II) sulfate pentahydrate (CuSO 4 ·5H 2 O)
• Spirit burner
• Optional: a muselet (this is a wire cage from a bottle of champagne or other sparkling wine) to act as a microscale tripod
• Activity 2 worksheet
• Put the bottle-top crucible on the balance and set it to 0.0 g using the tare button.
• Add about 1.2 g of hydrated copper sulfate to the crucible. Record the mass on the student worksheet.
• Optional: place the muselet on the spirit burner. This will act as a microscale tripod for the bottle top.
• Place the bottle-top crucible on the muselet tripod and heat with an ignited spirit burner until the blue colour has been lost and white/colourless solid remains. Alternatively, hold the bottle top with tongs and heat as above.

• Remove the crucible from the spirit burner (and extinguish the flame).
• Allow the crucible to cool.
• Find the mass of the crucible plus anhydrous salt and record this.
• Use the masses of hydrated and anhydrous copper sulfate to calculate the number of moles of copper sulfate and water present in the hydrated complex. The molar ratio can be used to determine the formula of hydrated copper(II) sulfate.

Mass of hydrated copper(II) sulfate used =

Mass of anhydrous copper(II) sulfate after heating =

Mass of water removed by heating = 1.20 − 0.78 =

Number of moles of copper sulfate (CuSO 4 ) left after water was removed  = 0.78 ⁄ 159.6 =

Number of moles of water removed by heating = 0.42 ⁄ 18 =

Ratio of moles of water to copper sulfate = 0.023 ⁄ 0.0049 = 4.9, which is 5 when rounded to nearest whole number.

The students can be shown the label of a bottle of hydrated copper sulfate and compare their result with the label to verify their result. The value should be close to five, giving a molar ratio of approximately five moles of water to one mole of copper(II) sulfate, which suggests the formula of the hydrated copper(II) sulfate is CuSO 4 ·5H 2 O.

Adrian and Bob have now reached the end of this series of articles on microscale chemistry. What started out as an antidote to the safety concerns of dealing with chemicals in schools, (storage, use, disposal) by education managers and the UK Health and Safety executive, in around 1993, has now attracted more enthusiasts because of the educational and economic benefits the techniques bring. Now we can add the promotion of sustainability, as directed by the United Nations, using the principles of green chemistry, [ 4 ] as formulated in 1998 by Paul Anastas and John C. Warner. At least 6 of the 12 principles of green chemistry can apply to school-taught chemistry. [ 5 ]

• Prevention of waste : droplets of solutions added to a plastic surface using transfer pipettes are just wiped away with a paper towel.
• Less-hazardous chemical syntheses : preparing copper sulfate crystals while avoiding scalds, burns, and the evolution of toxic gases; microelectrolysis of copper chloride solution.
• Safer solvents and auxiliaries : using water as the main solvent, as well as adopting salting-out procedures.
• Design for energy efficiency : spirit burners and hot water from a kettle can be used to avoid the use of fossil fuels (e.g., the Bunsen burner); using more energy efficient LEDs.
• Catalysis : using yeast to produce oxygen from hydrogen peroxide.
• Inherently safer chemistry for accident prevention :reducing concentrations, finding an alternative procedure to carry out the electrolysis of a molten lead bromide, and conducting small-scale catalytic cracking to avoid suck back.

This last principle is what CLEAPSS and SSERC in the UK have been doing since 1963.

We are often accused of removing the ‘wow’ moments that school chemistry brings. With the microchemistry approach, there are still explosions (dynamite soap bubbles), and there are more wow moments, such as the beauty of an array of colours in droplet art. [ 6 ] There are completely new demonstrations. Bob recently carried out a demonstration showing the electrical conductivity of molten sodium chloride , an observation that is quoted in many school texts as evidence of ionic bonding, but never easily demonstrated until now, by using microscale techniques [ 7 ] and the bottle-top crucible described in this article.

Acknowledgements

We would like to thank and acknowledge Howard Tolliday at Dornoch Academy, UK, for his advice and assistance in developing the equipment and his help in collecting the images and video accompanying this article.

[1] Worley B, Paterson D (2021) Understanding Chemistry through Microscale Practical Work pp 38-41. Association for Science Education. ISBN: 978-0863574788

[2] The Science on Stage webinar on microscale chemistry: https://youtu.be/LM97yXJlotQ?si=e_IGnqLuTiJdPV84

[3] The Royal Society of Chemistry resource to teach the cracking of long-chain hydrocarbons: https://edu.rsc.org/exhibition-chemistry/cracking/4010515.article

[4] The 12 principles of green chemistry: https://www.compoundchem.com/2015/09/24/green-chemistry/

[5] Green chemistry principles applicable in school chemistry: https://microchemuk.weebly.com/green-chemistry.html

[6] An article on chemical droplet art: https://uwaterloo.ca/chem13-news-magazine/september-2019/feature/indicator-droplet-art

[7] A video on the electrolysis of molten sodium chloride: https://www.youtube.com/watch?v=wKgDJYY6Vkk&t=60s

• Watch a webinar on microscale-chemistry techniques.
• Read about the 12 principles of green chemistry .
• Read an introduction to microscale chemistry in the classroom: Worley B (2021) Little wonder: microscale chemistry in the classroom . Science in School 53 .
• Discover simple adaptations of experiments to make chemistry accessible to students with vision impairment: Chataway-Green R, Schnepp Z (2023) Making chemistry accessible for students with vision impairment . Science in School 64 .
• Enhance your students’ understanding of electrolysis using microscale chemistry techniques: Worley B, Allan A (2022) Elegant electrolysis – the microscale way . Science in School 60 .
• Use microscale techniques to do quantitative chemistry experiments: Worley B, Allan A (2023) Quick quantitative chemistry – the microscale way . Science in School 63 .
• Teach the chemistry of precipitation using microscale-chemistry methods: Worley B, Allan A (2022) Pleasing precipitation performances – the microscale way . Science in School 57 .
• Make chemistry practice fun with chemical card games: Johnson P (2024) Stealth learning – how chemical card games can improve student participation . Science in School 68 .
• Use geometry to estimate the CO 2 absorbed by a tree in the schoolyard: Schwarz A et al. (2024) How much carbon is locked in that tree? Science in School 67 .
• Try some experiments with gases to illustrate stoichiometric reactions and combustion: Paternotte I, Wilock P (2022) Playing with fire: stoichiometric reactions and gas combustion . Science in School 59 .
• Promote critical thinking by adding some variables to the classic candle-mystery experiment: Ka Kit Yu S (2024) A twist on the candle mystery . Science in School 66 .
• Explore laboratory safety with creative horror stories about lab disasters: Havaste P, Hlaj J (2024) Lab disasters: creative learning through storytelling . Science in School 68 .
• Try a classroom activity to extract essential oils from fragrant plants: Allan A, Worley B, Owen M (2018) Perfumes with a pop: aroma chemistry with essential oils . Science in School 44 : 40–46.
• Read about the environmental costs of fireworks: Le Guillou I (2021) The dark side of fireworks . Science in School 55 .

Dr Adrian Allan is a teacher of chemistry at Dornoch Academy, UK. He was selected to represent the UK at the Science on Stage conferences in 2017 and 2019. He has presented Science on Stage webinars and workshops around Europe on microscale chemistry and using magic to teach science.

Bob Worley, FRSC, is the (semiretired) chemistry advisor for CLEAPSS in the UK. He taught chemistry for 20 years, and in 1991, he joined CLEAPSS, which provides safety and advisory support for classroom experiments. In carrying out these duties, he gained an interest in miniaturizing experiments to improve safety and convenience. He was awarded the 2021 Excellence in Secondary and Further Education Prize for significant and sustained contributions to the development and promotion of safe practical resources for teachers worldwide.

Microscale chemistry (and science in general!) is an incredibly important field.  The work done by Bob and Adrian is second to none, and will allow teacher (and learners) from all areas to get “stuck in” with micro.  From a budget and safety stand point, it makes all the sense in the world to approach thing on a microscale, especially in the current financial climate.

John Cochrane, Chemistry Teacher. Greenfaulds High School, Scotland.

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Want to see a cool trick? Make a tiny battery with these 3 household items

Emily Kwong

Rebecca Ramirez

Madeline K. Sofia

Electrical circuit can be created with lemons to power a small light source. A chemical reaction between the copper and zinc plates and the citric acid produces a small current, thus powering a light bulb. Andriy Onufriyenko/Getty Images hide caption

Electrical circuit can be created with lemons to power a small light source. A chemical reaction between the copper and zinc plates and the citric acid produces a small current, thus powering a light bulb.

We're going "Back to School" today, revisiting a classic at-home experiment that turns lemons into batteries — powerful enough to turn on a clock or a small lightbulb. But how does the science driving the "lemon battery" show up in those household batteries we use daily?

We get into just that today with environmental engineer Jenelle Fortunato about the fundamentals of electric currents and the inner workings of batteries.

You can build your very own lemon battery using Science U's design here , written by Fortunato and Christopher Gorski of Penn State College of Engineering.

A reminder: Do NOT play with household batteries. Be safe out there, scientists!

Want us to cover more science basics? Email us your ideas at [email protected] — we might feature them on a future episode!

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This episode was originally produced by Rebecca Ramirez and edited by Viet Le. The encore version was produced and edited by Rebecca Ramirez.

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🧪 Science with Sarah: Bubble Snakes 🧼🐍

Bountiful bubbles of fun.

Sarah Spivey , KSAT Weather Authority Meteorologist

Ben Spicer , Digital Journalist

👉 Watch the video of Sarah’s science experiment at Adams Hill Elementary here!

Hello parents, teachers and students! Sarah’s back in schools this fall semester, teaching kiddos about the joys of science! Today’s experiment is all about how gasses and liquids can combine to create a super cool bubble snake!

Be sure to check out GMSA@9 on Wednesdays when Meteorologist Sarah Spivey does the demonstrations and explains the science behind it.

HERE’S WHAT YOU’LL NEED

• An empty water bottle
• An old wash cloth or sock
• Rubber band
• Food dye (Optional)

DO THE EXPERIMENT

• STEP 1: Using the scissors and adult supervision, cut the water bottle in half and keep the top half
• STEP 2: Using a rubber band, secure the washcloth to the half top of the water bottle
• STEP 3: In the small bowl, mix dish soap and water
• STEP 4: Optional - “paint” the wash cloth with different colored food dye
• STEP 5: Dip the washcloth in the soapy water
• STEP 6: Using the mouth of the water bottle, blow and watch your bubble snake form!

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About the Authors

Sarah Spivey

Sarah Spivey is a San Antonio native who grew up watching KSAT. She has been a proud member of the KSAT Weather Authority Team since 2017. Sarah is a Clark High School and Texas A&M University graduate. She previously worked at KTEN News. When Sarah is not busy forecasting, she enjoys hanging out with her husband and cat, and playing music.

Ben Spicer is a digital journalist who works the early morning shift for KSAT.

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Science News

A nuclear clock prototype hints at ultraprecise timekeeping .

The device could allow for new tests of fundamental physics

In a new experiment, physicists used a laser (shown) to probe a jump between two energy levels in thorium-229, which could serve as a nuclear clock.

Chuankun Zhang/JILA

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By Emily Conover

9 hours ago

Scientific clockmakers have crafted a prototype of a nuclear clock, hinting at future possibilities for using atomic nuclei to perform precise measurements of time and make new tests of fundamental physics theories.

While the definition of a “clock” is scientifically hazy, the prototype is not yet used to measure time. So it technically should be called a “frequency standard,” physicist Jun Ye says. But the work brings scientists closer to a nuclear clock than ever before. “For the first time, all essential ingredients for a working nuclear clock are contained in this work,” says Ye, of JILA in Boulder, Colo. 

Whereas atomic clocks measure time based on electrons jumping between energy levels in atoms, nuclear clocks’ timekeeping would depend on the energy levels of atomic nuclei. A certain frequency of laser light is needed for an atom or an atomic nucleus to make such a jump. The wiggling of that light’s electromagnetic waves can be used to mark time. 

Nuclear clocks would keep time using a variety of the element thorium, called thorium-229. Most atomic nuclei make energy leaps that are too large to be triggered by a tabletop laser. But thorium-229 has two energy levels that are close enough to each other that the transition between those two levels could serve as a clock. 

Now, researchers have precisely determined the frequency of the light  needed to set off that jump. It’s 2,020,407,384,335 kilohertz, Ye and colleagues report in the Sept. 5  Nature.

Importantly, the measurement   has an uncertainty of 2 kilohertz. That’s more than a million times the precision of the best previous measurement. And it’s more than a billion times the precision to which that frequency was known just over a year ago, highlighting multiple back-to-back developments. 

The improvement hinged on a component called a  frequency comb  ( SN: 10/5/18 ). A crucial component of many atomic clocks, a frequency comb creates an array of discrete frequencies of light. Using a frequency comb with thorium-229 has been a  major research goal , for some scientists ( SN: 6/4/21 ). In the new work, Ye and colleagues compared the nuclear clock transition with that of an atomic clock with a known frequency. 

“This is something that will be important as a scientific application for tests of fundamental physics,” says physicist Ekkehard Peik of the National Metrology Institute in Braunschweig, Germany, who was not involved with the new research. 

In the future, such comparisons could be used to search for strange physics effects, such as  drifting of the values of fundamental constants  ( SN: 11/2/16 ). These are numbers that — as the name implies — are believed to be eternally unwavering.

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