scientific method controlled experiment

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, generate accurate citations for free.

Knowledge Base

Methodology

What Is a Controlled Experiment? | Definitions & Examples

What Is a Controlled Experiment? | Definitions & Examples

Published on April 19, 2021 by Pritha Bhandari . Revised on June 22, 2023.

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

Controlling variables can involve:

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

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

Control in experiments is critical for internal validity , which allows you to establish a cause-and-effect relationship between variables. Strong validity also helps you avoid research biases , particularly ones related to issues with generalizability (like sampling bias and selection bias .)

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

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

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

Prevent plagiarism. Run a free check.

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

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

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

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

Control groups

Controlled experiments require control groups . Control groups allow you to test a comparable treatment, no treatment, or a fake treatment (e.g., a placebo to control for a placebo effect ), and compare the outcome with your experimental treatment.

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

To test the effect of colors in advertising, each participant is placed in one of two groups:

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

Random assignment

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

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

Random assignment is a hallmark of a “true experiment”—it differentiates true experiments from quasi-experiments .

Masking (blinding)

Masking in experiments means hiding condition assignment from participants or researchers—or, in a double-blind study , from both. It’s often used in clinical studies that test new treatments or drugs and is critical for avoiding several types of research bias .

Sometimes, researchers may unintentionally encourage participants to behave in ways that support their hypotheses , leading to observer bias . In other cases, cues in the study environment may signal the goal of the experiment to participants and influence their responses. These are called demand characteristics . If participants behave a particular way due to awareness of being observed (called a Hawthorne effect ), your results could be invalidated.

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

You use an online survey form to present the advertisements to participants, and you leave the room while each participant completes the survey on the computer so that you can’t tell which condition each participant was in.

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

Difficult to control all variables

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

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

Risk of low external validity

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

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

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

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

Student’s t -distribution
Normal distribution
Null and Alternative Hypotheses
Chi square tests
Confidence interval
Quartiles & Quantiles
Cluster sampling
Stratified sampling
Data cleansing
Reproducibility vs Replicability
Peer review
Prospective cohort study

Research bias

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

Here's why students love Scribbr's proofreading services

Discover proofreading & editing

In a controlled experiment , all extraneous variables are held constant so that they can’t influence the results. Controlled experiments require:

A control group that receives a standard treatment, a fake treatment, or no treatment.
Random assignment of participants to ensure the groups are equivalent.

Depending on your study topic, there are various other methods of controlling variables .

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.

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.

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator.

Bhandari, P. (2023, June 22). What Is a Controlled Experiment? | Definitions & Examples. Scribbr. Retrieved June 18, 2024, from https://www.scribbr.com/methodology/controlled-experiment/

Is this article helpful?

Pritha Bhandari

Other students also liked, extraneous variables | examples, types & controls, guide to experimental design | overview, steps, & examples, how to write a lab report, what is your plagiarism score.

What Is a Controlled Experiment?

Definition and Example

Scientific Method
Chemical Laws
Periodic Table
Projects & Experiments
Biochemistry
Physical Chemistry
Medical Chemistry
Chemistry In Everyday Life
Famous Chemists
Activities for Kids
Abbreviations & Acronyms
Weather & Climate
Ph.D., Biomedical Sciences, University of Tennessee at Knoxville
B.A., Physics and Mathematics, Hastings College

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

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

Controlled Experiment

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

Example of a Controlled Experiment

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

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

Why Controlled Experiments Are Important

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

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

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

Are All Experiments Controlled?

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

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

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

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

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

Box, George E. P., et al. Statistics for Experimenters: Design, Innovation, and Discovery . Wiley-Interscience, a John Wiley & Soncs, Inc., Publication, 2005.
Creswell, John W. Educational Research: Planning, Conducting, and Evaluating Quantitative and Qualitative Research . Pearson/Merrill Prentice Hall, 2008.
Pronzato, L. "Optimal experimental design and some related control problems". Automatica . 2008.
Robbins, H. "Some Aspects of the Sequential Design of Experiments". Bulletin of the American Mathematical Society . 1952.
Examples of Independent and Dependent Variables
Difference Between Independent and Dependent Variables
Null Hypothesis Examples
Chemistry Glassware Types, Names and Uses
What Is the Difference Between a Control Variable and Control Group?
Understanding Simple vs Controlled Experiments
What are Controlled Experiments?
Understanding Experimental Groups
The Difference Between Control Group and Experimental Group
The Role of a Controlled Variable in an Experiment
What Is an Experiment? Definition and Design
What Is a Control Group?
Scientific Method Vocabulary Terms
Scientific Variable
8th Grade Science Fair Project Ideas
What Is a Double Blind Experiment?

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, automatically generate references for free.

Knowledge Base
Methodology
Controlled Experiments | Methods & Examples of Control

Controlled Experiments | Methods & Examples of Control

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

Controlling variables can involve:

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

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

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

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

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

Prevent plagiarism, run a free check.

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

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

Control groups

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

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

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

Random assignment

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

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

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

Masking (blinding)

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

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

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

Difficult to control all variables

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

Risk of low external validity

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

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

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

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

To design a successful experiment, first identify:

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

When designing the experiment, first decide:

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

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the ‘Cite this Scribbr article’ button to automatically add the citation to our free Reference Generator.

Bhandari, P. (2022, October 10). Controlled Experiments | Methods & Examples of Control. Scribbr. Retrieved 18 June 2024, from https://www.scribbr.co.uk/research-methods/controlled-experiments/

Is this article helpful?

Pritha Bhandari

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Publications
Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

Advanced Search
Journal List
v.20(10); 2019 Oct 4

Why control an experiment?

John s torday.

1 Department of Pediatrics, Harbor‐UCLA Medical Center, Torrance, CA, USA

František Baluška

2 IZMB, University of Bonn, Bonn, Germany

Empirical research is based on observation and experimentation. Yet, experimental controls are essential for overcoming our sensory limits and generating reliable, unbiased and objective results.

An external file that holds a picture, illustration, etc.
Object name is EMBR-20-e49110-g001.jpg

We made a deliberate decision to become scientists and not philosophers, because science offers the opportunity to test ideas using the scientific method. And once we began our formal training as scientists, the greatest challenge beyond formulating a testable or refutable hypothesis was designing appropriate controls for an experiment. In theory, this seems trivial, but in practice, it is often difficult. But where and when did this concept of controlling an experiment start? It is largely attributed to Roger Bacon, who emphasized the use of artificial experiments to provide additional evidence for observations in his Novum Organum Scientiarum in 1620. Other philosophers took up the concept of empirical research: in 1877, Charles Peirce redefined the scientific method in The Fixation of Belief as the most efficient and reliable way to prove a hypothesis. In the 1930s, Karl Popper emphasized the necessity of refuting hypotheses in The Logic of Scientific Discoveries . While these influential works do not explicitly discuss controls as an integral part of experiments, their importance for generating solid and reliable results is nonetheless implicit.

… once we began our formal training as scientists, the greatest challenge beyond formulating a testable or refutable hypothesis was designing appropriate controls for an experiment.

But the scientific method based on experimentation and observation has come under criticism of late in light of the ever more complex problems faced in physics and biology. Chris Anderson, the editor of Wired Magazine, proposed that we should turn to statistical analysis, machine learning, and pattern recognition instead of creating and testing hypotheses, based on the Informatics credo that if you cannot answer the question, you need more data. However, this attitude subsumes that we already have enough data and that we just cannot make sense of it. This assumption is in direct conflict with David Bohm's thesis that there are two “Orders”, the Explicate and Implicate 1 . The Explicate Order is the way in which our subjective sensory systems perceive the world 2 . In contrast, Bohm's Implicate Order would represent the objective reality beyond our perception. This view—that we have only a subjective understanding of reality—dates back to Galileo Galilei who, in 1623, criticized the Aristotelian concept of absolute and objective qualities of our sensory perceptions 3 and to Plato's cave allegory that reality is only what our senses allow us to see.

The only way for systematically overcoming the limits of our sensory apparatus and to get a glimpse of the Implicate Order is through the scientific method, through hypothesis‐testing, controlled experimentation. Beyond the methodology, controlling an experiment is critically important to ensure that the observed results are not just random events; they help scientists to distinguish between the “signal” and the background “noise” that are inherent in natural and living systems. For example, the detection method for the recent discovery of gravitational waves used four‐dimensional reference points to factor out the background noise of the Cosmos. Controls also help to account for errors and variability in the experimental setup and measuring tools: The negative control of an enzyme assay, for instance, tests for any unrelated background signals from the assay or measurement. In short, controls are essential for the unbiased, objective observation and measurement of the dependent variable in response to the experimental setup.

The only way for systematically overcoming the limits of our sensory apparatus […] is through the Scientific Method, through hypothesis‐testing, controlled experimentation.

Nominally, both positive and negative controls are material and procedural; that is, they control for variability of the experimental materials and the procedure itself. But beyond the practical issues to avoid procedural and material artifacts, there is an underlying philosophical question. The need for experimental controls is a subliminal recognition of the relative and subjective nature of the Explicate Order. It requires controls as “reference points” in order to transcend it, and to approximate the Implicate Order.

This is similar to Peter Rowlands’ 4 dictum that everything in the Universe adds up to zero, the universal attractor in mathematics. Prior to the introduction of zero, mathematics lacked an absolute reference point similar to a negative or positive control in an experiment. The same is true of biology, where the cell is the reference point owing to its negative entropy: It appears as an attractor for the energy of its environment. Hence, there is a need for careful controls in biology: The homeostatic balance that is inherent to life varies during the course of an experiment and therefore must be precisely controlled to distinguish noise from signal and approximate the Implicate Order of life.

P < 0.05 tacitly acknowledges the explicate order

Another example of the “subjectivity” of our perception is the level of accuracy we accept for differences between groups. For example, when we use statistical methods to determine if an observed difference between control and experimental groups is a random occurrence or a specific effect, we conventionally consider a p value of less than or equal to 5% as statistically significant; that is, there is a less than 0.05 probability that the effect is random. The efficacy of this arbitrary convention has been debated for decades; suffice to say that despite questioning the validity of that convention, a P value of < 0.05 reflects our acceptance of the subjectivity of our perception of reality.

… controls are essential for the unbiased, objective observation and measurement of the dependent variable in response to the experimental setup.

Thus, if we do away with hypothesis‐testing science in favor of informatics based on data and statistics—referring to Anderson's suggestion—it reflects our acceptance of the noise in the system. However, mere data analysis without any underlying hypothesis is tantamount to “garbage in‐garbage out”, in contrast to well‐controlled imaginative experiments to separate the wheat from the chaff. Albert Einstein was quoted as saying that imagination was more important than knowledge.

The ultimate purpose of the scientific method is to understand ourselves and our place in Nature. Conventionally, we subscribe to the Anthropic Principle, that we are “in” this Universe, whereas the Endosymbiosis Theory, advocated by Lynn Margulis, stipulates that we are “of” this Universe as a result of the assimilation of the physical environment. According to this theory, the organism endogenizes external factors to make them physiologically “useful”, such as iron as the core of the hemoglobin molecule, or ancient bacteria as mitochondria.

… there is a fundamental difference between knowing via believing and knowing based on empirical research.

By applying the developmental mechanism of cell–cell communication to phylogeny, we have revealed the interrelationships between cells and explained evolution from its origin as the unicellular state to multicellularity via cell–cell communication. The ultimate outcome of this research is that consciousness is the product of cellular processes and cell–cell communication in order to react to the environment and better anticipate future events 5 , 6 . Consciousness is an essential prerequisite for transcending the Explicate Order toward the Implicate Order via cellular sensory and cognitive systems that feed an ever‐expanding organismal knowledge about both the environment and itself.

It is here where the empirical approach to understanding nature comes in with its emphasis that knowledge comes only from sensual experience rather than innate ideas or traditions. In the context of the cell or higher systems, knowledge about the environment can only be gained by sensing and analyzing the environment. Empiricism is similar to an equation in which the variables and terms form a product, or a chemical reaction, or a biological process where the substrates, aka sensory data, form products, that is, knowledge. However, it requires another step—imagination, according to Albert Einstein—to transcend the Explicate Order in order to gain insight into the Implicate Order. Take for instance, Dmitri Ivanovich Mendeleev's Periodic Table of Elements: his brilliant insight was not just to use Atomic Number to organize it, but also to consider the chemical reactivities of the Elements by sorting them into columns. By introducing chemical reactivity to the Periodic Table, Mendeleev provided something like the “fourth wall” in Drama, which gives the audience an omniscient, god‐like perspective on what is happening on stage.

The capacity to transcend the subjective Explicate Order to approximate the objective Implicate Order is not unlike Eastern philosophies like Buddhism or Taoism, which were practiced long before the scientific method. An Indian philosopher once pointed out that the Hindus have known for 30,000 years that the Earth revolves around the sun, while the Europeans only realized this a few hundred years ago based on the work of Copernicus, Brahe, and Galileo. However, there is a fundamental difference between knowing via believing and knowing based on empirical research. A similar example is Aristotle's refusal to test whether a large stone would fall faster than a small one, as he knew the answer already 7 . Galileo eventually performed the experiment from the Leaning Tower in Pisa to demonstrate that the fall time of two objects is independent of their mass—which disproved Aristotle's theory of gravity that stipulated that objects fall at a speed proportional to their mass. Again, it demonstrates the power of empiricism and experimentation as formulated by Francis Bacon, John Locke, and others, over intuition and rationalizing.

Even if our scientific instruments provide us with objective data, we still need to apply our consciousness to evaluate and interpret such data.

Following the evolution from the unicellular state to multicellular organisms—and reverse‐engineering it to a minimal‐cell state—reveals that biologic diversity is an artifact of the Explicate Order. Indeed, the unicell seems to be the primary level of selection in the Implicate Order, as it remains proximate to the First Principles of Physiology, namely negative entropy (negentropy), chemiosmosis, and homeostasis. The first two principles are necessary for growth and proliferation, whereas the last reflects Newton's Third Law of Motion that every action has an equal and opposite reaction so as to maintain homeostasis.

All organisms interact with their surroundings and assimilate their experience as epigenetic marks. Such marks extend to the DNA of germ cells and thus change the phenotypic expression of the offspring. The offspring, in turn, interacts with the environment in response to such epigenetic modifications, giving rise to the concept of the phenotype as an agent that actively and purposefully interacts with its environment in order to adapt and survive. This concept of phenotype based on agency linked to the Explicate Order fundamentally differs from its conventional description as a mere set of biologic characteristics. Organisms’ capacities to anticipate future stress situations from past memories are obvious in simple animals such as nematodes, as well as in plants and bacteria 8 , suggesting that the subjective Explicate Order controls both organismal behavior and trans‐generational evolution.

That perspective offers insight to the nature of consciousness: not as a “mind” that is separate from a “body”, but as an endogenization of physical matter, which complies with the Laws of Nature. In other words, consciousness is the physiologic manifestation of endogenized physical surroundings, compartmentalized, and made essential for all organisms by forming the basis for their physiology. Endocytosis and endocytic/synaptic vesicles contribute to endogenization of cellular surroundings, allowing eukaryotic organisms to gain knowledge about the environment. This is true not only for neurons in brains, but also for all eukaryotic cells 5 .

Such a view of consciousness offers insight to our awareness of our physical surroundings as the basis for self‐referential self‐organization. But this is predicated on our capacity to “experiment” with our environment. The burgeoning idea that we are entering the Anthropocene, a man‐made world founded on subjective senses instead of Natural Laws, is a dangerous step away from our innate evolutionary arc. Relying on just our senses and emotions, without experimentation and controls to understand the Implicate Order behind reality, is not just an abandonment of the principles of the Enlightenment, but also endangers the planet and its diversity of life.

Acknowledgements

John Torday has been a recipient of NIH Grant HL055268. František Baluška is thankful to numerous colleagues for very stimulating discussions on topics analyzed in this article.

EMBO Reports (2019) 20 : e49110 [ Google Scholar ]

Contributor Information

John S Torday, Email: ude.alcu@yadrotj .

František Baluška, Email: ed.nnob-inu@aksulab .

COVID-19 Tracker
Biochemistry
Anatomy & Physiology
Microbiology
Neuroscience
Animal Kingdom
NGSS High School
Latest News
Editors’ Picks
Weekly Digest
Quotes about Biology

Controlled Experiment

Reviewed by: BD Editors

Controlled Experiment Definition

A controlled experiment is a scientific test that is directly manipulated by a scientist, in order to test a single variable at a time. The variable being tested is the independent variable , and is adjusted to see the effects on the system being studied. The controlled variables are held constant to minimize or stabilize their effects on the subject. In biology, a controlled experiment often includes restricting the environment of the organism being studied. This is necessary to minimize the random effects of the environment and the many variables that exist in the wild.

In a controlled experiment, the study population is often divided into two groups. One group receives a change in a certain variable, while the other group receives a standard environment and conditions. This group is referred to as the control group , and allows for comparison with the other group, known as the experimental group . Many types of controls exist in various experiments, which are designed to ensure that the experiment worked, and to have a basis for comparison. In science, results are only accepted if it can be shown that they are statistically significant . Statisticians can use the difference between the control group and experimental group and the expected difference to determine if the experiment supports the hypothesis , or if the data was simply created by chance.

Examples of Controlled Experiment

Music preference in dogs.

Do dogs have a taste in music? You might have considered this, and science has too. Believe it or not, researchers have actually tested dog’s reactions to various music genres. To set up a controlled experiment like this, scientists had to consider the many variables that affect each dog during testing. The environment the dog is in when listening to music, the volume of the music, the presence of humans, and even the temperature were all variables that the researches had to consider.

In this case, the genre of the music was the independent variable. In other words, to see if dog’s change their behavior in response to different kinds of music, a controlled experiment had to limit the interaction of the other variables on the dogs. Usually, an experiment like this is carried out in the same location, with the same lighting, furniture, and conditions every time. This ensures that the dogs are not changing their behavior in response to the room. To make sure the dogs don’t react to humans or simply the noise of the music, no one else can be in the room and the music must be played at the same volume for each genre. Scientist will develop protocols for their experiment, which will ensure that many other variables are controlled.

This experiment could also split the dogs into two groups, only testing music on one group. The control group would be used to set a baseline behavior, and see how dogs behaved without music. The other group could then be observed and the differences in the group’s behavior could be analyzed. By rating behaviors on a quantitative scale, statistics can be used to analyze the difference in behavior, and see if it was large enough to be considered significant. This basic experiment was carried out on a large number of dogs, analyzing their behavior with a variety of different music genres. It was found that dogs do show more relaxed and calm behaviors when a specific type of music plays. Come to find out, dogs enjoy reggae the most.

Scurvy in Sailors

In the early 1700s, the world was a rapidly expanding place. Ships were being built and sent all over the world, carrying thousands and thousands of sailors. These sailors were mostly fed the cheapest diets possible, not only because it decreased the costs of goods, but also because fresh food is very hard to keep at sea. Today, we understand that lack of essential vitamins and nutrients can lead to severe deficiencies that manifest as disease. One of these diseases is scurvy.

Scurvy is caused by a simple vitamin C deficiency, but the effects can be brutal. Although early symptoms just include general feeling of weakness, the continued lack of vitamin C will lead to a breakdown of the blood cells and vessels that carry the blood. This results in blood leaking from the vessels. Eventually, people bleed to death internally and die. Before controlled experiments were commonplace, a simple physician decided to tackle the problem of scurvy. James Lind, of the Royal Navy, came up with a simple controlled experiment to find the best cure for scurvy.

He separated sailors with scurvy into various groups. He subjected them to the same controlled condition and gave them the same diet, except one item. Each group was subjected to a different treatment or remedy, taken with their food. Some of these remedies included barley water, cider and a regiment of oranges and lemons. This created the first clinical trial , or test of the effectiveness of certain treatments in a controlled experiment. Lind found that the oranges and lemons helped the sailors recover fast, and within a few years the Royal Navy had developed protocols for growing small leafy greens that contained high amounts of vitamin C to feed their sailors.

Related Biology Terms

Field Experiment – An experiment conducted in nature, outside the bounds of total control.
Independent Variable – The thing in an experiment being changed or manipulated by the experimenter to see effects on the subject.
Controlled Variable – A thing that is normalized or standardized across an experiment, to remove it from having an effect on the subject being studied.
Control Group – A group of subjects in an experiment that receive no independent variable, or a normalized amount, to provide comparison.

1. Why is it necessary for scientist to conduct controlled experiments? A. They allow for more definite relationships of cause and effect to be established B. Without control, anything could happen in the experiment C. Safety first! Controls are just silly safety precautions

2. A population of birds is being studied. Which of the following describes a controlled experiment on the birds? A. The birds are subjected to a lighting strike, and the results observed. B. The birds are split in two groups, one left in the wild and one kept in captivity. After time, the effects are measured and compared. C. The birds are observed as they migrate from North to South America.

3. A scientist is interested in the effects of a pesticide on the eggs of certain birds. The scientist takes some eggs and coats them with pesticide, and does not coat a second group of eggs. He places them in an incubator for several days, then measures several chemical and physical properties of the eggs. Is this a good controlled experiment? A. Yes B. No C. Maybe, but there may be more to the picture

Cite This Article

Subscribe to our newsletter, privacy policy, terms of service, scholarship, latest posts, white blood cell, t cell immunity, satellite cells, embryonic stem cells, popular topics, natural selection, adenosine triphosphate (atp), pituitary gland, mitochondria, water cycle.

The Scientific Method Tutorial

The Scientific Method

Steps in the scientific method.

There is a great deal of variation in the specific techniques scientists use explore the natural world. However, the following steps characterize the majority of scientific investigations:

Step 1: Make observations Step 2: Propose a hypothesis to explain observations Step 3: Test the hypothesis with further observations or experiments Step 4: Analyze data Step 5: State conclusions about hypothesis based on data analysis

Each of these steps is explained briefly below, and in more detail later in this section.

Step 1: Make observations

A scientific inquiry typically starts with observations. Often, simple observations will trigger a question in the researcher's mind.

Example: A biologist frequently sees monarch caterpillars feeding on milkweed plants, but rarely sees them feeding on other types of plants. She wonders if it is because the caterpillars prefer milkweed over other food choices.

Step 2: Propose a hypothesis

The researcher develops a hypothesis (singular) or hypotheses (plural) to explain these observations. A hypothesis is a tentative explanation of a phenomenon or observation(s) that can be supported or falsified by further observations or experimentation.

Example: The researcher hypothesizes that monarch caterpillars prefer to feed on milkweed compared to other common plants. (Notice how the hypothesis is a statement, not a question as in step 1.)

Step 3: Test the hypothesis

The researcher makes further observations and/or may design an experiment to test the hypothesis. An experiment is a controlled situation created by a researcher to test the validity of a hypothesis. Whether further observations or an experiment is used to test the hypothesis will depend on the nature of the question and the practicality of manipulating the factors involved.

Example: The researcher sets up an experiment in the lab in which a number of monarch caterpillars are given a choice between milkweed and a number of other common plants to feed on.

Step 4: Analyze data

The researcher summarizes and analyzes the information, or data, generated by these further observations or experiments.

Example: In her experiment, milkweed was chosen by caterpillars 9 times out of 10 over all other plant selections.

Step 5: State conclusions

The researcher interprets the results of experiments or observations and forms conclusions about the meaning of these results. These conclusions are generally expressed as probability statements about their hypothesis.

Example: She concludes that when given a choice, 90 percent of monarch caterpillars prefer to feed on milkweed over other common plants.

Often, the results of one scientific study will raise questions that may be addressed in subsequent research. For example, the above study might lead the researcher to wonder why monarchs seem to prefer to feed on milkweed, and she may plan additional experiments to explore this question. For example, perhaps the milkweed has higher nutritional value than other available plants.

Return to top of page

The Scientific Method Flowchart

The steps in the scientific method are presented visually in the following flow chart. The question raised or the results obtained at each step directly determine how the next step will proceed. Following the flow of the arrows, pass the cursor over each blue box. An explanation and example of each step will appear. As you read the example given at each step, see if you can predict what the next step will be.

Activity: Apply the Scientific Method to Everyday Life Use the steps of the scientific method described above to solve a problem in real life. Suppose you come home one evening and flick the light switch only to find that the light doesn’t turn on. What is your hypothesis? How will you test that hypothesis? Based on the result of this test, what are your conclusions? Follow your instructor's directions for submitting your response.

The above flowchart illustrates the logical sequence of conclusions and decisions in a typical scientific study. There are some important points to note about this process:

1. The steps are clearly linked.

The steps in this process are clearly linked. The hypothesis, formed as a potential explanation for the initial observations, becomes the focus of the study. The hypothesis will determine what further observations are needed or what type of experiment should be done to test its validity. The conclusions of the experiment or further observations will either be in agreement with or will contradict the hypothesis. If the results are in agreement with the hypothesis, this does not prove that the hypothesis is true! In scientific terms, it "lends support" to the hypothesis, which will be tested again and again under a variety of circumstances before researchers accept it as a fairly reliable description of reality.

2. The same steps are not followed in all types of research.

The steps described above present a generalized method followed in a many scientific investigations. These steps are not carved in stone. The question the researcher wishes to answer will influence the steps in the method and how they will be carried out. For example, astronomers do not perform many experiments as defined here. They tend to rely on observations to test theories. Biologists and chemists have the ability to change conditions in a test tube and then observe whether the outcome supports or invalidates their starting hypothesis, while astronomers are not able to change the path of Jupiter around the Sun and observe the outcome!

3. Collected observations may lead to the development of theories.

When a large number of observations and/or experimental results have been compiled, and all are consistent with a generalized description of how some element of nature operates, this description is called a theory. Theories are much broader than hypotheses and are supported by a wide range of evidence. Theories are important scientific tools. They provide a context for interpretation of new observations and also suggest experiments to test their own validity. Theories are discussed in more detail in another section.

. .

The Scientific Method in Detail

In the sections that follow, each step in the scientific method is described in more detail.

Step 1: Observations

Observations in science.

An observation is some thing, event, or phenomenon that is noticed or observed. Observations are listed as the first step in the scientific method because they often provide a starting point, a source of questions a researcher may ask. For example, the observation that leaves change color in the fall may lead a researcher to ask why this is so, and to propose a hypothesis to explain this phenomena. In fact, observations also will provide the key to answering the research question.

In science, observations form the foundation of all hypotheses, experiments, and theories. In an experiment, the researcher carefully plans what observations will be made and how they will be recorded. To be accepted, scientific conclusions and theories must be supported by all available observations. If new observations are made which seem to contradict an established theory, that theory will be re-examined and may be revised to explain the new facts. Observations are the nuts and bolts of science that researchers use to piece together a better understanding of nature.

Observations in science are made in a way that can be precisely communicated to (and verified by) other researchers. In many types of studies (especially in chemistry, physics, and biology), quantitative observations are used. A quantitative observation is one that is expressed and recorded as a quantity, using some standard system of measurement. Quantities such as size, volume, weight, time, distance, or a host of others may be measured in scientific studies.

Some observations that researchers need to make may be difficult or impossible to quantify. Take the example of color. Not all individuals perceive color in exactly the same way. Even apart from limiting conditions such as colorblindness, the way two people see and describe the color of a particular flower, for example, will not be the same. Color, as perceived by the human eye, is an example of a qualitative observation.

Qualitative observations note qualities associated with subjects or samples that are not readily measured. Other examples of qualitative observations might be descriptions of mating behaviors, human facial expressions, or "yes/no" type of data, where some factor is present or absent. Though the qualities of an object may be more difficult to describe or measure than any quantities associated with it, every attempt is made to minimize the effects of the subjective perceptions of the researcher in the process. Some types of studies, such as those in the social and behavioral sciences (which deal with highly variable human subjects), may rely heavily on qualitative observations.

Question: Why are observations important to science?

Limits of Observations

Because all observations rely to some degree on the senses (eyes, ears, or steady hand) of the researcher, complete objectivity is impossible. Our human perceptions are limited by the physical abilities of our sense organs and are interpreted according to our understanding of how the world works, which can be influenced by culture, experience, or education. According to science education specialist, George F. Kneller, "Surprising as it may seem, there is no fact that is not colored by our preconceptions" ("A Method of Enquiry," from Science and Its Ways of Knowing [Upper Saddle River: Prentice-Hall Inc., 1997], 15).

Observations made by a scientist are also limited by the sensitivity of whatever equipment he is using. Research findings will be limited at times by the available technology. For example, Italian physicist and philosopher Galileo Galilei (1564–1642) was reportedly the first person to observe the heavens with a telescope. Imagine how it must have felt to him to see the heavens through this amazing new instrument! It opened a window to the stars and planets and allowed new observations undreamed of before.

In the centuries since Galileo, increasingly more powerful telescopes have been devised that dwarf the power of that first device. In the past decade, we have marveled at images from deep space , courtesy of the Hubble Space Telescope, a large telescope that orbits Earth. Because of its view from outside the distorting effects of the atmosphere, the Hubble can look 50 times farther into space than the best earth-bound telescopes, and resolve details a tenth of the size (Seeds, Michael A., Horizons: Exploring the Universe , 5 th ed. [Belmont: Wadsworth Publishing Company, 1998], 86-87).

Construction is underway on a new radio telescope that scientists say will be able to detect electromagnetic waves from the very edges of the universe! This joint U.S.-Mexican project may allow us to ask questions about the origins of the universe and the beginnings of time that we could never have hoped to answer before. Completion of the new telescope is expected by the end of 2001.

Although the amount of detail observed by Galileo and today's astronomers is vastly different, the stars and their relationships have not changed very much. Yet with each technological advance, the level of detail of observation has been increased, and with it, the power to answer more and more challenging questions with greater precision.

Question: What are some of the differences between a casual observation and a 'scientific observation'?

Step 2: The Hypothesis

A hypothesis is a statement created by the researcher as a potential explanation for an observation or phenomena. The hypothesis converts the researcher's original question into a statement that can be used to make predictions about what should be observed if the hypothesis is true. For example, given the hypothesis, "exposure to ultraviolet (UV) radiation increases the risk of skin cancer," one would predict higher rates of skin cancer among people with greater UV exposure. These predictions could be tested by comparing skin cancer rates among individuals with varying amounts of UV exposure. Note how the hypothesis itself determines what experiments or further observations should be made to test its validity. Results of tests are then compared to predictions from the hypothesis, and conclusions are stated in terms of whether or not the data supports the hypothesis. So the hypothesis serves a guide to the full process of scientific inquiry.

The Qualities of a Good Hypothesis

A hypothesis must be testable or provide predictions that are testable. It can potentially be shown to be false by further observations or experimentation.
A hypothesis should be specific. If it is too general it cannot be tested, or tests will have so many variables that the results will be complicated and difficult to interpret. A well-written hypothesis is so specific it actually determines how the experiment should be set up.
A hypothesis should not include any untested assumptions if they can be avoided. The hypothesis itself may be an assumption that is being tested, but it should be phrased in a way that does not include assumptions that are not tested in the experiment.
It is okay (and sometimes a good idea) to develop more than one hypothesis to explain a set of observations. Competing hypotheses can often be tested side-by-side in the same experiment.

Question: Why is the hypothesis important to the scientific method?

grow well in a lighted incubator maintained at 90 F. A culture of was accidentally left uncovered overnight on a laboratory bench where it was dark and temperatures fluctuated between 65 F and 68 F. When the technician returned in the morning, all the cells were dead. Which of the following statements is the hypothesis to explain why the cells died, based on this observation?		cells to die.

Step 3: Testing the Hypothesis

A hypothesis may be tested in one of two ways: by making additional observations of a natural situation, or by setting up an experiment. In either case, the hypothesis is used to make predictions, and the observations or experimental data collected are examined to determine if they are consistent or inconsistent with those predictions. Hypothesis testing, especially through experimentation, is at the core of the scientific process. It is how scientists gain a better understanding of how things work.

Testing a Hypothesis by Observation

Some hypotheses may be tested through simple observation. For example, a researcher may formulate the hypothesis that the sun always rises in the east. What might an alternative hypothesis be? If his hypothesis is correct, he would predict that the sun will rise in the east tomorrow. He can easily test such a prediction by rising before dawn and going out to observe the sunrise. If the sun rises in the west, he will have disproved the hypothesis. He will have shown that it does not hold true in every situation. However, if he observes on that morning that the sun does in fact rise in the east, he has not proven the hypothesis. He has made a single observation that is consistent with, or supports, the hypothesis. As a scientist, to confidently state that the sun will always rise in the east, he will want to make many observations, under a variety of circumstances. Note that in this instance no manipulation of circumstance is required to test the hypothesis (i.e., you aren't altering the sun in any way).

Testing a Hypothesis by Experimentation

An experiment is a controlled series of observations designed to test a specific hypothesis. In an experiment, the researcher manipulates factors related to the hypothesis in such a way that the effect of these factors on the observations (data) can be readily measured and compared. Most experiments are an attempt to define a cause-and-effect relationship between two factors or events—to explain why something happens. For example, with the hypothesis "roses planted in sunny areas bloom earlier than those grown in shady areas," the experiment would be testing a cause-and-effect relationship between sunlight and time of blooming.

A major advantage of setting up an experiment versus making observations of what is already available is that it allows the researcher to control all the factors or events related to the hypothesis, so that the true cause of an event can be more easily isolated. In all cases, the hypothesis itself will determine the way the experiment will be set up. For example, suppose my hypothesis is "the weight of an object is proportional to the amount of time it takes to fall a certain distance." How would you test this hypothesis?

The Qualities of a Good Experiment

The experiment must be conducted on a group of subjects that are narrowly defined and have certain aspects in common. This is the group to which any conclusions must later be confined. (Examples of possible subjects: female cancer patients over age 40, E. coli bacteria, red giant stars, the nicotine molecule and its derivatives.)
All subjects of the experiment should be (ideally) completely alike in all ways except for the factor or factors that are being tested. Factors that are compared in scientific experiments are called variables. A variable is some aspect of a subject or event that may differ over time or from one group of subjects to another. For example, if a biologist wanted to test the effect of nitrogen on grass growth, he would apply different amounts of nitrogen fertilizer to several plots of grass. The grass in each of the plots should be as alike as possible so that any difference in growth could be attributed to the effect of the nitrogen. For example, all the grass should be of the same species, planted at the same time and at the same density, receive the same amount of water and sunlight, and so on. The variable in this case would be the amount of nitrogen applied to the plants. The researcher would not compare differing amounts of nitrogen across different grass species to determine the effect of nitrogen on grass growth. What is the problem with using different species of plants to compare the effect of nitrogen on plant growth? There are different kinds of variables in an experiment. A factor that the experimenter controls, and changes intentionally to determine if it has an effect, is called an independent variable . A factor that is recorded as data in the experiment, and which is compared across different groups of subjects, is called a dependent variable . In many cases, the value of the dependent variable will be influenced by the value of an independent variable. The goal of the experiment is to determine a cause-and-effect relationship between independent and dependent variables—in this case, an effect of nitrogen on plant growth. In the nitrogen/grass experiment, (1) which factor was the independent variable? (2) Which factor was the dependent variable?
Nearly all types of experiments require a control group and an experimental group. The control group generally is not changed in any way, but remains in a "natural state," while the experimental group is modified in some way to examine the effect of the variable which of interest to the researcher. The control group provides a standard of comparison for the experimental groups. For example, in new drug trials, some patients are given a placebo while others are given doses of the drug being tested. The placebo serves as a control by showing the effect of no drug treatment on the patients. In research terminology, the experimental groups are often referred to as treatments , since each group is treated differently. In the experimental test of the effect of nitrogen on grass growth, what is the control group? In the example of the nitrogen experiment, what is the purpose of a control group?
In research studies a great deal of emphasis is placed on repetition. It is essential that an experiment or study include enough subjects or enough observations for the researcher to make valid conclusions. The two main reasons why repetition is important in scientific studies are (1) variation among subjects or samples and (2) measurement error.

Variation among Subjects

There is a great deal of variation in nature. In a group of experimental subjects, much of this variation may have little to do with the variables being studied, but could still affect the outcome of the experiment in unpredicted ways. For example, in an experiment designed to test the effects of alcohol dose levels on reflex time in 18- to 22-year-old males, there would be significant variation among individual responses to various doses of alcohol. Some of this variation might be due to differences in genetic make-up, to varying levels of previous alcohol use, or any number of factors unknown to the researcher.

Because what the researcher wants to discover is average dose level effects for this group, he must run the test on a number of different subjects. Suppose he performed the test on only 10 individuals. Do you think the average response calculated would be the same as the average response of all 18- to 22-year-old males? What if he tests 100 individuals, or 1,000? Do you think the average he comes up with would be the same in each case? Chances are it would not be. So which average would you predict would be most representative of all 18- to 22-year-old males?

A basic rule of statistics is, the more observations you make, the closer the average of those observations will be to the average for the whole population you are interested in. This is because factors that vary among a population tend to occur most commonly in the middle range, and least commonly at the two extremes. Take human height for example. Although you may find a man who is 7 feet tall, or one who is 4 feet tall, most men will fall somewhere between 5 and 6 feet in height. The more men we measure to determine average male height, the less effect those uncommon extreme (tall or short) individuals will tend to impact the average. Thus, one reason why repetition is so important in experiments is that it helps to assure that the conclusions made will be valid not only for the individuals tested, but also for the greater population those individuals represent.

"The use of a sample (or subset) of a population, an event, or some other aspect of nature for an experimental group that is not large enough to be representative of the whole" is called sampling error (Starr, Cecie, Biology: Concepts and Applications , 4 th ed. [Pacific Cove: Brooks/Cole, 2000], glossary). If too few samples or subjects are used in an experiment, the researcher may draw incorrect conclusions about the population those samples or subjects represent.

Use the jellybean activity below to see a simple demonstration of samping error.

Directions: There are 400 jellybeans in the jar. If you could not see the jar and you initially chose 1 green jellybean from the jar, you might assume the jar only contains green jelly beans. The jar actually contains both green and black jellybeans. Use the "pick 1, 5, or 10" buttons to create your samples. For example, use the "pick" buttons now to create samples of 2, 13, and 27 jellybeans. After you take each sample, try to predict the ratio of green to black jellybeans in the jar. How does your prediction of the ratio of green to black jellybeans change as your sample changes?

Measurement Error

The second reason why repetition is necessary in research studies has to do with measurement error. Measurement error may be the fault of the researcher, a slight difference in measuring techniques among one or more technicians, or the result of limitations or glitches in measuring equipment. Even the most careful researcher or the best state-of-the-art equipment will make some mistakes in measuring or recording data. Another way of looking at this is to say that, in any study, some measurements will be more accurate than others will. If the researcher is conscientious and the equipment is good, the majority of measurements will be highly accurate, some will be somewhat inaccurate, and a few may be considerably inaccurate. In this case, the same reasoning used above also applies here: the more measurements taken, the less effect a few inaccurate measurements will have on the overall average.

Step 4: Data Analysis

In any experiment, observations are made, and often, measurements are taken. Measurements and observations recorded in an experiment are referred to as data . The data collected must relate to the hypothesis being tested. Any differences between experimental and control groups must be expressed in some way (often quantitatively) so that the groups may be compared. Graphs and charts are often used to visualize the data and to identify patterns and relationships among the variables.

Statistics is the branch of mathematics that deals with interpretation of data. Data analysis refers to statistical methods of determining whether any differences between the control group and experimental groups are too great to be attributed to chance alone. Although a discussion of statistical methods is beyond the scope of this tutorial, the data analysis step is crucial because it provides a somewhat standardized means for interpreting data. The statistical methods of data analysis used, and the results of those analyses, are always included in the publication of scientific research. This convention limits the subjective aspects of data interpretation and allows scientists to scrutinize the working methods of their peers.

Why is data analysis an important step in the scientific method?

Step 5: Stating Conclusions

The conclusions made in a scientific experiment are particularly important. Often, the conclusion is the only part of a study that gets communicated to the general public. As such, it must be a statement of reality, based upon the results of the experiment. To assure that this is the case, the conclusions made in an experiment must (1) relate back to the hypothesis being tested, (2) be limited to the population under study, and (3) be stated as probabilities.

The hypothesis that is being tested will be compared to the data collected in the experiment. If the experimental results contradict the hypothesis, it is rejected and further testing of that hypothesis under those conditions is not necessary. However, if the hypothesis is not shown to be wrong, that does not conclusively prove that it is right! In scientific terms, the hypothesis is said to be "supported by the data." Further testing will be done to see if the hypothesis is supported under a number of trials and under different conditions.

If the hypothesis holds up to extensive testing then the temptation is to claim that it is correct. However, keep in mind that the number of experiments and observations made will only represent a subset of all the situations in which the hypothesis may potentially be tested. In other words, experimental data will only show part of the picture. There is always the possibility that a further experiment may show the hypothesis to be wrong in some situations. Also, note that the limits of current knowledge and available technologies may prevent a researcher from devising an experiment that would disprove a particular hypothesis.

The researcher must be sure to limit his or her conclusions to apply only to the subjects tested in the study. If a particular species of fish is shown to consume their young 90 percent of the time when raised in captivity, that doesn't necessarily mean that all fish will do so, or that this fish's behavior would be the same in its native habitat.

Finally, the conclusions of the experiment are generally stated as probabilities. A careful scientist would never say, "drug x kills cancer cells;" she would more likely say, "drug x was shown to destroy 85 percent of cancerous skin cells in rats in lab trials." Notice how very different these two statements are. There is a tendency in the media and in the general public to gravitate toward the first statement. This makes a terrific headline and is also easy to interpret; it is absolute. Remember though, in science conclusions must be confined to the population under study; broad generalizations should be avoided. The second statement is sound science. There is data to back it up. Later studies may reveal a more universal effect of the drug on cancerous cells, or they may not. Most researchers would be unwilling to stake their reputations on the first statement.

As a student, you should read and interpret popular press articles about research studies very carefully. From the text, can you determine how the experiment was set up and what variables were measured? Are the observations and data collected appropriate to the hypothesis being tested? Are the conclusions supported by the data? Are the conclusions worded in a scientific context (as probability statements) or are they generalized for dramatic effect? In any researched-based assignment, it is a good idea to refer to the original publication of a study (usually found in professional journals) and to interpret the facts for yourself.

Qualities of a Good Experiment

narrowly defined subjects
all subjects treated alike except for the factor or variable being studied
a control group is used for comparison
measurements related to the factors being studied are carefully recorded
enough samples or subjects are used so that conclusions are valid for the population of interest
conclusions made relate back to the hypothesis, are limited to the population being studied, and are stated in terms of probabilities

by Stephen S. Carey.

In order to continue enjoying our site, we ask that you confirm your identity as a human. Thank you very much for your cooperation.

Experimental Method In Psychology

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

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

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

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

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

On This Page:

The experimental method involves the manipulation of variables to establish cause-and-effect relationships. The key features are controlled methods and the random allocation of participants into controlled and experimental groups .

What is an Experiment?

An experiment is an investigation in which a hypothesis is scientifically tested. An independent variable (the cause) is manipulated in an experiment, and the dependent variable (the effect) is measured; any extraneous variables are controlled.

An advantage is that experiments should be objective. The researcher’s views and opinions should not affect a study’s results. This is good as it makes the data more valid and less biased.

There are three types of experiments you need to know:

1. Lab Experiment

A laboratory experiment in psychology is a research method in which the experimenter manipulates one or more independent variables and measures the effects on the dependent variable under controlled conditions.

A laboratory experiment is conducted under highly controlled conditions (not necessarily a laboratory) where accurate measurements are possible.

The researcher uses a standardized procedure to determine where the experiment will take place, at what time, with which participants, and in what circumstances.

Participants are randomly allocated to each independent variable group.

Examples are Milgram’s experiment on obedience and Loftus and Palmer’s car crash study .

Strength : It is easier to replicate (i.e., copy) a laboratory experiment. This is because a standardized procedure is used.
Strength : They allow for precise control of extraneous and independent variables. This allows a cause-and-effect relationship to be established.
Limitation : The artificiality of the setting may produce unnatural behavior that does not reflect real life, i.e., low ecological validity. This means it would not be possible to generalize the findings to a real-life setting.
Limitation : Demand characteristics or experimenter effects may bias the results and become confounding variables .

2. Field Experiment

A field experiment is a research method in psychology that takes place in a natural, real-world setting. It is similar to a laboratory experiment in that the experimenter manipulates one or more independent variables and measures the effects on the dependent variable.

However, in a field experiment, the participants are unaware they are being studied, and the experimenter has less control over the extraneous variables .

Field experiments are often used to study social phenomena, such as altruism, obedience, and persuasion. They are also used to test the effectiveness of interventions in real-world settings, such as educational programs and public health campaigns.

An example is Holfing’s hospital study on obedience .

Strength : behavior in a field experiment is more likely to reflect real life because of its natural setting, i.e., higher ecological validity than a lab experiment.
Strength : Demand characteristics are less likely to affect the results, as participants may not know they are being studied. This occurs when the study is covert.
Limitation : There is less control over extraneous variables that might bias the results. This makes it difficult for another researcher to replicate the study in exactly the same way.

3. Natural Experiment

A natural experiment in psychology is a research method in which the experimenter observes the effects of a naturally occurring event or situation on the dependent variable without manipulating any variables.

Natural experiments are conducted in the day (i.e., real life) environment of the participants, but here, the experimenter has no control over the independent variable as it occurs naturally in real life.

Natural experiments are often used to study psychological phenomena that would be difficult or unethical to study in a laboratory setting, such as the effects of natural disasters, policy changes, or social movements.

For example, Hodges and Tizard’s attachment research (1989) compared the long-term development of children who have been adopted, fostered, or returned to their mothers with a control group of children who had spent all their lives in their biological families.

Here is a fictional example of a natural experiment in psychology:

Researchers might compare academic achievement rates among students born before and after a major policy change that increased funding for education.

In this case, the independent variable is the timing of the policy change, and the dependent variable is academic achievement. The researchers would not be able to manipulate the independent variable, but they could observe its effects on the dependent variable.

Strength : behavior in a natural experiment is more likely to reflect real life because of its natural setting, i.e., very high ecological validity.
Strength : Demand characteristics are less likely to affect the results, as participants may not know they are being studied.
Strength : It can be used in situations in which it would be ethically unacceptable to manipulate the independent variable, e.g., researching stress .
Limitation : They may be more expensive and time-consuming than lab experiments.
Limitation : There is no control over extraneous variables that might bias the results. This makes it difficult for another researcher to replicate the study in exactly the same way.

Key Terminology

Ecological validity.

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

Experimenter effects

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

Demand characteristics

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

Independent variable (IV)

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

Dependent variable (DV)

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

Extraneous variables (EV)

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

Confounding variables

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

Random Allocation

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

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

Order effects

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

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

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

Research Methodology

Phenomenology In Qualitative Research

Ethnography In Qualitative Research

Narrative Analysis In Qualitative Research

Thematic Analysis: A Step by Step Guide

Metasynthesis Of Qualitative Research

Grounded Theory In Qualitative Research: A Practical Guide

Sciencing_Icons_Science SCIENCE

Sciencing_icons_biology biology, sciencing_icons_cells cells, sciencing_icons_molecular molecular, sciencing_icons_microorganisms microorganisms, sciencing_icons_genetics genetics, sciencing_icons_human body human body, sciencing_icons_ecology ecology, sciencing_icons_chemistry chemistry, sciencing_icons_atomic & molecular structure atomic & molecular structure, sciencing_icons_bonds bonds, sciencing_icons_reactions reactions, sciencing_icons_stoichiometry stoichiometry, sciencing_icons_solutions solutions, sciencing_icons_acids & bases acids & bases, sciencing_icons_thermodynamics thermodynamics, sciencing_icons_organic chemistry organic chemistry, sciencing_icons_physics physics, sciencing_icons_fundamentals-physics fundamentals, sciencing_icons_electronics electronics, sciencing_icons_waves waves, sciencing_icons_energy energy, sciencing_icons_fluid fluid, sciencing_icons_astronomy astronomy, sciencing_icons_geology geology, sciencing_icons_fundamentals-geology fundamentals, sciencing_icons_minerals & rocks minerals & rocks, sciencing_icons_earth scructure earth structure, sciencing_icons_fossils fossils, sciencing_icons_natural disasters natural disasters, sciencing_icons_nature nature, sciencing_icons_ecosystems ecosystems, sciencing_icons_environment environment, sciencing_icons_insects insects, sciencing_icons_plants & mushrooms plants & mushrooms, sciencing_icons_animals animals, sciencing_icons_math math, sciencing_icons_arithmetic arithmetic, sciencing_icons_addition & subtraction addition & subtraction, sciencing_icons_multiplication & division multiplication & division, sciencing_icons_decimals decimals, sciencing_icons_fractions fractions, sciencing_icons_conversions conversions, sciencing_icons_algebra algebra, sciencing_icons_working with units working with units, sciencing_icons_equations & expressions equations & expressions, sciencing_icons_ratios & proportions ratios & proportions, sciencing_icons_inequalities inequalities, sciencing_icons_exponents & logarithms exponents & logarithms, sciencing_icons_factorization factorization, sciencing_icons_functions functions, sciencing_icons_linear equations linear equations, sciencing_icons_graphs graphs, sciencing_icons_quadratics quadratics, sciencing_icons_polynomials polynomials, sciencing_icons_geometry geometry, sciencing_icons_fundamentals-geometry fundamentals, sciencing_icons_cartesian cartesian, sciencing_icons_circles circles, sciencing_icons_solids solids, sciencing_icons_trigonometry trigonometry, sciencing_icons_probability-statistics probability & statistics, sciencing_icons_mean-median-mode mean/median/mode, sciencing_icons_independent-dependent variables independent/dependent variables, sciencing_icons_deviation deviation, sciencing_icons_correlation correlation, sciencing_icons_sampling sampling, sciencing_icons_distributions distributions, sciencing_icons_probability probability, sciencing_icons_calculus calculus, sciencing_icons_differentiation-integration differentiation/integration, sciencing_icons_application application, sciencing_icons_projects projects, sciencing_icons_news news.

Share Tweet Email Print
Home ⋅
Math ⋅
Probability & Statistics ⋅
Independent/Dependent Variables

Definitions of Control, Constant, Independent and Dependent Variables in a Science Experiment

Why Should You Only Test for One Variable at a Time in an Experiment?

The point of an experiment is to help define the cause and effect relationships between components of a natural process or reaction. The factors that can change value during an experiment or between experiments, such as water temperature, are called scientific variables, while those that stay the same, such as acceleration due to gravity at a certain location, are called constants.

The scientific method includes three main types of variables: constants, independent, and dependent variables. In a science experiment, each of these variables define a different measured or constrained aspect of the system.

Constant Variables

Experimental constants are values that should not change either during or between experiments. Many natural forces and properties, such as the speed of light and the atomic weight of gold, are experimental constants. In some cases, a property can be considered constant for the purposes of an experiment even though it technically could change under certain circumstances. The boiling point of water changes with altitude and acceleration due to gravity decreases with distance from the earth, but for experiments in one location these can also be considered constants.

Sometimes also called a controlled variable. A constant is a variable that could change, but that the experimenter intentionally keeps constant in order to more clearly isolate the relationship between the independent variable and the dependent variable.

If extraneous variables are not properly constrained, they are referred to as confounding variables, as they interfere with the interpretation of the results of the experiment.

Some examples of control variables might be found with an experiment examining the relationship between the amount of sunlight plants receive (independent variable) and subsequent plant growth (dependent variable). The experiment should control the amount of water the plants receive and when, what type of soil they are planted in, the type of plant, and as many other different variables as possible. This way, only the amount of light is being changed between trials, and the outcome of the experiment can be directly applied to understanding only this relationship.

Independent Variable

The independent variable in an experiment is the variable whose value the scientist systematically changes in order to see what effect the changes have. A well-designed experiment has only one independent variable in order to maintain a fair test. If the experimenter were to change two or more variables, it would be harder to explain what caused the changes in the experimental results. For example, someone trying to find how quickly water boils could alter the volume of water or the heating temperature, but not both.

Dependent Variable

A dependent variable – sometimes called a responding variable – is what the experimenter observes to find the effect of systematically varying the independent variable. While an experiment may have multiple dependent variables, it is often wisest to focus the experiment on one dependent variable so that the relationship between it and the independent variable can be clearly isolated. For example, an experiment could examine how much sugar can dissolve in a set volume of water at various temperatures. The experimenter systematically alters temperature (independent variable) to see its effect on the quantity of dissolved sugar (dependent variable).

Control Groups

In some experiment designs, there might be one effect or manipulated variable that is being measured. Sometimes there might be one collection of measurements or subjects completely separated from this variable called the control group. These control groups are held as a standard to measure the results of a scientific experiment.

An example of such a situation might be a study regarding the effectiveness of a certain medication. There might be multiple experimental groups that receive the medication in varying doses and applications, and there would likely be a control group that does not receive the medication at all.

Representing Results

Identifying which variables are independent, dependent, and controlled helps to collect data, perform useful experiments, and accurately communicate results. When graphing or displaying data, it is crucial to represent data accurately and understandably. Typically, the independent variable goes on the x-axis, and the dependent variable goes on the y-axis.

Why should you only test for one variable at a time..., difference between manipulative & responding variable, what is the meaning of variables in research, what is the difference between a control & a controlled..., what are constants & controls of a science project..., what is a constant in a science fair project, what are comparative experiments, how to grow a plant from a bean as a science project, how to calculate experimental value, what is an independent variable in quantitative research, what is a responding variable in science projects, what is a positive control in microbiology, how to write a testable hypothesis, how to use the pearson correlation coefficient, 5 components of a well-designed scientific experiment, distinguishing between descriptive & causal studies, how to write a protocol for biology experiments, what is a standardized variable in biology, school science projects for juniors, how to write a summary on a science project.

ScienceBuddies.org: Variables in Your Science Fair Project

About the Author

Benjamin Twist has worked as a writer, editor and consultant since 2007. He writes fiction and nonfiction for online and print publications, as well as offering one-on-one writing consultations and tutoring. Twist holds a Master of Arts in Bible exposition from Columbia International University.

Find Your Next Great Science Fair Project! GO

Science Notes Posts
Contact Science Notes
Todd Helmenstine Biography
Anne Helmenstine Biography
Free Printable Periodic Tables (PDF and PNG)
Periodic Table Wallpapers
Interactive Periodic Table
Periodic Table Posters
Science Experiments for Kids
How to Grow Crystals
Chemistry Projects
Fire and Flames Projects
Holiday Science
Chemistry Problems With Answers
Physics Problems
Unit Conversion Example Problems
Chemistry Worksheets
Biology Worksheets
Periodic Table Worksheets
Physical Science Worksheets
Science Lab Worksheets
My Amazon Books

What Is a Control Variable? Definition and Examples

A control variable is any factor that is controlled or held constant in an experiment.

A control variable is any factor that is controlled or held constant during an experiment . For this reason, it’s also known as a controlled variable or a constant variable. A single experiment may contain many control variables . Unlike the independent and dependent variables , control variables aren’t a part of the experiment, but they are important because they could affect the outcome. Take a look at the difference between a control variable and control group and see examples of control variables.

Importance of Control Variables

Remember, the independent variable is the one you change, the dependent variable is the one you measure in response to this change, and the control variables are any other factors you control or hold constant so that they can’t influence the experiment. Control variables are important because:

They make it easier to reproduce the experiment.
The increase confidence in the outcome of the experiment.

For example, if you conducted an experiment examining the effect of the color of light on plant growth, but you didn’t control temperature, it might affect the outcome. One light source might be hotter than the other, affecting plant growth. This could lead you to incorrectly accept or reject your hypothesis. As another example, say you did control the temperature. If you did not report this temperature in your “methods” section, another researcher might have trouble reproducing your results. What if you conducted your experiment at 15 °C. Would you expect the same results at 5 °C or 35 5 °C? Sometimes the potential effect of a control variable can lead to a new experiment!

Sometimes you think you have controlled everything except the independent variable, but still get strange results. This could be due to what is called a “ confounding variable .” Examples of confounding variables could be humidity, magnetism, and vibration. Sometimes you can identify a confounding variable and turn it into a control variable. Other times, confounding variables cannot be detected or controlled.

Control Variable vs Control Group

A control group is different from a control variable. You expose a control group to all the same conditions as the experimental group, except you change the independent variable in the experimental group. Both the control group and experimental group should have the same control variables.

Control Variable Examples

Anything you can measure or control that is not the independent variable or dependent variable has potential to be a control variable. Examples of common control variables include:

Duration of the experiment
Size and composition of containers
Temperature
Sample volume
Experimental technique
Chemical purity or manufacturer
Species (in biological experiments)

For example, consider an experiment testing whether a certain supplement affects cattle weight gain. The independent variable is the supplement, while the dependent variable is cattle weight. A typical control group would consist of cattle not given the supplement, while the cattle in the experimental group would receive the supplement. Examples of control variables in this experiment could include the age of the cattle, their breed, whether they are male or female, the amount of supplement, the way the supplement is administered, how often the supplement is administered, the type of feed given to the cattle, the temperature, the water supply, the time of year, and the method used to record weight. There may be other control variables, too. Sometimes you can’t actually control a control variable, but conditions should be the same for both the control and experimental groups. For example, if the cattle are free-range, weather might change from day to day, but both groups have the same experience. When you take data, be sure to record control variables along with the independent and dependent variable.

Box, George E.P.; Hunter, William G.; Hunter, J. Stuart (1978). Statistics for Experimenters : An Introduction to Design, Data Analysis, and Model Building . New York: Wiley. ISBN 978-0-471-09315-2.
Giri, Narayan C.; Das, M. N. (1979). Design and Analysis of Experiments . New York, N.Y: Wiley. ISBN 9780852269145.
Stigler, Stephen M. (November 1992). “A Historical View of Statistical Concepts in Psychology and Educational Research”. American Journal of Education . 101 (1): 60–70. doi: 10.1086/444032

Grades 6-12
School Leaders

100 Last-Day-of-School Activities Your Students Will Love!

What Are the Scientific Method Steps?

Explore with a well-organized and curious approach.

The scientific method not only teaches students how to conduct experiments, but it also enables them to think critically about processes that extend beyond science and into all aspects of their academic lives. Just like detectives, scientists, and explorers, students can use this scientific method structured-steps approach to explore, question, and discover.

What is the scientific method?

What are the steps of the scientific method, how does the scientific method encourage critical thinking, how are the scientific method steps used in the classroom.

Free printable scientific method steps worksheet
Free printable scientific method steps posters

The scientific method is like a structured adventure for exploring the world that encourages discovery by finding answers and solving puzzles. With the scientific method steps, students get to ask questions, observe, make educated guesses (called hypotheses), run experiments, collect and organize data, draw sensible conclusions, and share what they’ve learned. Students can explore the natural world with a well-organized and curious approach.

The scientific method steps can vary by name, but the process as a whole is the same across grade levels. There are as many as seven steps, but sometimes they are combined. Below are six steps that make the process accessible to younger learners.

1. Question

Encourage students to ask why, what, when, where, or how about a particular phenomenon or topic. Get them wondering about something that they find interesting or have a passion for.

2. Research

Teach them to use their senses to gather information and make notes—for example, what are they seeing, hearing, etc.

3. Hypothesize

Based on observations, students will then make a hypothesis, which is an educated guess—it’s what they think will happen in an experiment.

4. Experiment

To test their hypothesis, students can conduct an investigation or experiment and collect data. Data collection can involve charts, graphs, and observations.

Students can then look at the results of their experiment and interpret what that means in the grand scheme of their original question. From the data collected, students can then apply the new knowledge to their original question.

Just like real scientists, students can communicate their findings with their classmates in a presentation, lab write-up, and many other ways.

Be sure to check out our free printable scientific method posters and free scientific method steps printable .

The scientific method fosters critical thinking in students by promoting curiosity, observation, hypothesis formation, problem-solving, data analysis, logical reasoning, and effective communication. This structured approach equips students with vital skills for science and everyday life, while also promoting open-mindedness, adaptability, and reflective thinking, enhancing their critical thinking abilities across various situations.

The scientific method isn’t just about experiments, it’s a valuable tool that helps students become critical thinkers in all areas of their studies. From forming hypotheses to conducting experiments and sharing findings, it equips them with important skills. Plus, it encourages open-mindedness and adaptability. By using the scientific method, students start a lifelong adventure of learning and solving problems.

Even students as young as kindergarten can begin learning and exploring the scientific method steps. Plus, the scientific method is used all the way through high school and beyond, so it’s not a one-and-done skill. If you’re looking for hands-on ways for students to practice the scientific method, we compiled science experiments, labs, and demonstrations for elementary through middle school teachers to share with their students:

Kindergarten Experiments and Projects
1st Grade Experiments and Projects
2nd Grade Experiments and Projects
3rd Grade Experiments and Projects
4th Grade Experiments and Projects
5th Grade Experiments and Projects
6th Grade Experiments and Projects
7th Grade Experiments and Projects
8th Grade Experiments and Projects
High School Experiments and Projects

Free Printable Scientific Method Worksheet

This worksheet includes space for students to fill in every step of the scientific inquiry process along with prompts to ensure they stay on track.

Free Printable Scientific Method Posters

Looking for a visual aid to help your students remember the steps to the scientific method? Get our free printable scientific method posters.

Unleash the power of the scientific method in elementary and middle school with examples of scientific method steps and free printables.

Grab Your Free Scientific Method Worksheet Printable

Supercharge scientific inquiry. Continue Reading

Controlled Experiments: Methods, Examples & Limitations

What happens in experimental research is that the researcher alters the independent variables so as to determine their impacts on the dependent variables.

Therefore, when the experiment is controlled, you can expect that the researcher will control all other variables except for the independent variables . This is done so that the other variables do not have an influence on the dependent variables.

In this article, we are going to consider controlled experiment, how important it is in a study, and how it can be designed. But before we dig deep, let us look at the definition of a controlled experiment.

What is a Controlled Experiment?

In a scientific experiment, a controlled experiment is a test that is directly altered by the researcher so that only one variable is studied at a time. The single variable being studied will then be the independent variable.

This independent variable is manipulated by the researcher so that its effect on the hypothesis or data being studied is known. While the researcher studies the single independent variable, the controlled variables are made constant to reduce or balance out their impact on the research.

To achieve a controlled experiment, the research population is mostly distributed into two groups. Then the treatment is administered to one of the two groups, while the other group gets the control conditions. This other group is referred to as the control group.

The control group gets the standard conditions and is placed in the standard environment and it also allows for comparison with the other group, which is referred to as the experimental group or the treatment group. Obtaining the difference between these two groups’ behavior is important because in any scientific experiment, being able to show the statistical significance of the results is the only criterion for the results to be accepted.

So to determine whether the experiment supports the hypothesis, or if the data is a result of chance, the researcher will check for the difference between the control group and experimental group. Then the results from the differences will be compared with the expected difference.

For example, a researcher may want to answer this question, do dogs also have a music taste? In case you’re wondering too, yes, there are existing studies by researchers on how dogs react to different music genres.

Back to the example, the researcher may develop a controlled experiment with high consideration on the variables that affect each dog. Some of these variables that may have effects on the dog are; the dog’s environment when listening to music, the temperature of the environment, the music volume, and human presence.

The independent variable to focus on in this research is the genre of the music. To determine if there is an effect on the dog while listening to different kinds of music, the dog’s environment must be controlled. A controlled experiment would limit interaction between the dog and other variables.

In this experiment, the researcher can also divide the dogs into two groups, one group will perform the music test while the other, the control group will be used as the baseline or standard behavior. The control group behavior can be observed along with the treatment group and the differences in the two group’s behavior can be analyzed.

What is an Experimental Control?

Experimental control is the technique used by the researcher in scientific research to minimize the effects of extraneous variables. Experimental control also strengthens the ability of the independent variable to change the dependent variable.

For example, the cause and effect possibilities will be examined in a well-designed and properly controlled experiment if the independent variable (Treatment Y) causes a behavioral change in the dependent variable (Subject X).

In another example, a researcher feeds 20 lab rats with an artificial sweetener and from the researcher’s observation, six of the rats died of dehydration. Now, the actual cause of death may be artificial sweeteners or an unrelated factor. Such as the water supplied to the rats being contaminated or the rats could not drink enough, or suffering a disease.

Read: Nominal, Ordinal, Interval & Ratio Variable + [Examples]

For a researcher, eliminating these potential causes one after the other will consume time, and be tedious. Hence, the researcher can make use of experimental control. This method will allow the researcher to divide the rats into two groups: one group will receive the artificial sweetener while the other one doesn’t. The two groups will be placed in similar conditions and observed in similar ways. The differences that now occur in morbidity between the two groups can be traced to the sweetener with certainty.

From the example above, the experimental control is administered as a form of a control group. The data from the control group is then said to be the standard against which every other experimental outcome is measured.

Purpose & Importance of Control in Experimentation

1. One significant purpose of experimental controls is that it allows researchers to eliminate various confounding variables or uncertainty in their research. A researcher will need to use an experimental control to ensure that only the variables that are intended to change, are changed in research.

2. Controlled experiments also allow researchers to control the specific variables they think might have an effect on the outcomes of the study. The researcher will use a control group if he/she believes some extra variables can form an effect on the results of the study. This is to ensure that the extra variable is held constant and possible influences are measured.

3. Controlled experiments establish a standard that the outcome of a study should be compared to, and allow researchers to correct for potential errors.

Read more: What are Cross-Sectional Studies: Examples, Definition, Types

Methods of Experimental Control

Here are some methods used to achieve control in experimental research

Use of Control Groups

Control groups are required for controlled experiments. Control groups will allow the researcher to run a test on fake treatment, and comparable treatment. It will also compare the result of the comparison with the researcher’s experimental treatment. The results will allow the researcher to understand if the treatment administered caused the outcome or if other factors such as time, or others are involved and whether they would have yielded the same effects.

For an example of a control group experiment, a researcher conducting an experiment on the effects of colors in advertising, asked all the participants to come individually to a lab. In this lab, environmental conditions are kept the same all through the research.

For the researcher to determine the effect of colors in advertising, each of the participants is placed in either of the two groups: the control group or the experimental group.

In the control group, the advertisement color is yellow to represent the clothing industry while blue is given as the advertisement color to the experimental group to represent the clothing industry also. The only difference in these two groups will be the color of the advertisement, other variables will be similar.

Use of Masking (blinding)

Masking occurs in an experiment when the researcher hides condition assignments from the participants. If it’s double-blind research, both the researcher and the participants will be in the dark. Masking or blinding is mostly used in clinical studies to test new treatments.

Masking as a control measure takes place because sometimes, researchers may unintentionally influence the participants to act in ways that support their hypotheses. In another scenario, the goal of the study might be revealed to the participants through the study environment and this may influence their responses.

Masking, however, blinds the participants from having a deeper knowledge of the research whether they’re in the control group or the experimental group. This helps to control and reduce biases from either the researcher or the participants that could influence the results of the study.

Use of Random Assignment

Random assignment or distribution is used to avoid systematic differences between participants in the experimental group and the control group. This helps to evenly distribute extraneous participant variables, thereby making the comparison between groups valid. Another usefulness of random assignment is that it shows the difference between true experiments from quasi-experiments.

Learn About: Double-Blind Studies in Research: Types, Pros & Cons

How to Design a Controlled Experiment

For a researcher to design a controlled experiment, the researcher will need:

A hypothesis that can be tested.
One or more independent variables can be changed or manipulated precisely.
One or more dependent variables can be accurately measured.

Then, when the researcher is designing the experiment, he or she must decide on:

How will the variables be manipulated?
How will control be set up in case of any potential confounding variables?
How large will the samples or participants included in the study be?
How will the participants be distributed into treatment levels?

How you design your experimental control is highly significant to your experiment’s external and internal validity.

Controlled Experiment Examples

1. A good example of a controlled group would be an experiment to test the effects of a drug. The sample population would be divided into two, the group receiving the drug would be the experimental group while the group receiving the placebo would be the control group (Note that all the variables such as age, and sex, will be the same).

The only significant difference between the two groups will be the taking of medication. You can determine if the drug is effective or not if the control group and experimental group show similar results.

2. Let’s take a look at this example too. If a researcher wants to determine the impact of different soil types on the germination period of seeds, the researcher can proceed to set up four different pots. Each of the pots would be filled with a different type of soil and then seeds can be planted on the soil. After which each soil pot will be watered and exposed to sunlight.

The researcher will start to measure how long it took for the seeds to sprout in each of the different soil types. Control measures for this experiment might be to place some seeds in a pot without filling the pot with soil. The reason behind this control measure is to determine that no other factor is responsible for germination except the soil.

Here, the researcher can also control the amount of sun the seeds are exposed to, or how much water they are given. The aim is to eliminate all other variables that can affect how quickly the seeds sprouted.

Experimental controls are important, but it is also important to note that not all experiments should be controlled and It is still possible to get useful data from experiments that are not controlled.

Explore: 21 Chrome Extensions for Academic Researchers in 2021

Problems with Controlled Experiments

It is true that the best way to test for cause and effect relationships is by conducting controlled experiments. However, controlled experiments also have some challenges. Some of which are:

Difficulties in controlling all the variables especially when the participants in your research are human participants. It can be impossible to hold all the extra variables constant because all individuals have different experiences that may influence their behaviors.
Controlled experiments are at risk of low external validity because there’s a limit to how the results from the research can be extrapolated to a very large population .
Your research may lack relatability to real world experience if they are too controlled and that will make it hard for you to apply your outcomes outside a controlled setting.

Control Group vs an Experimental Group

There is a thin line between the control group and the experimental group. That line is the treatment condition. As we have earlier established, the experimental group is the one that gets the treatment while the control group is the placebo group.

All controlled experiments require control groups because control groups will allow you to compare treatments, and to test if there is no treatment while you compare the result with your experimental treatment.

Therefore, both the experimental group and the control group are required to conduct a controlled experiment

FAQs about Controlled Experiments

Is the control condition the same as the control group?

The control group is different from the control condition. However, the control condition is administered to the control group.

What are positive and negative control in an experiment?

The negative control is the group where no change or response is expected while the positive control is the group that receives the treatment with a certainty of a positive result.

While the controlled experiment is beneficial to eliminate extraneous variables in research and focus on the independent variable only to cause an effect on the dependent variable.

Researchers should be careful so they don’t lose real-life relatability to too controlled experiments and also, not all experiments should be controlled.

Connect to Formplus, Get Started Now - It's Free!

cofounding variables
control groups
controlled experiment
controlled experiment examples
controlled experiments
experimental control
extraneous variables
busayo.longe

Extraneous Variables Explained: Types & Examples

In this article, we are going to discuss extraneous variables and how they impact research.

Lurking Variables Explained: Types & Examples

In this article, we’ll discuss what a lurking variable means, the several types available, its effects along with some real-life examples

Descriptive Research Designs: Types, Examples & Methods

Ultimate guide to Descriptive research. Definitions, designs, types, examples and methodology.

How to do a Meta Analysis: Methodology, Pros & Cons

In this article, we’ll go through the concept of meta-analysis, what it can be used for, and how you can use it to improve how you...

Formplus - For Seamless Data Collection

Collect data the right way with a versatile data collection tool. try formplus and transform your work productivity today..

What is a scientific hypothesis?

It's the initial building block in the scientific method.

A girl looks at plants in a test tube for a science experiment. What's her scientific hypothesis?

Hypothesis basics

What makes a hypothesis testable.

Types of hypotheses
Hypothesis versus theory

Additional resources

Bibliography.

A scientific hypothesis is a tentative, testable explanation for a phenomenon in the natural world. It's the initial building block in the scientific method . Many describe it as an "educated guess" based on prior knowledge and observation. While this is true, a hypothesis is more informed than a guess. While an "educated guess" suggests a random prediction based on a person's expertise, developing a hypothesis requires active observation and background research.

The basic idea of a hypothesis is that there is no predetermined outcome. For a solution to be termed a scientific hypothesis, it has to be an idea that can be supported or refuted through carefully crafted experimentation or observation. This concept, called falsifiability and testability, was advanced in the mid-20th century by Austrian-British philosopher Karl Popper in his famous book "The Logic of Scientific Discovery" (Routledge, 1959).

A key function of a hypothesis is to derive predictions about the results of future experiments and then perform those experiments to see whether they support the predictions.

A hypothesis is usually written in the form of an if-then statement, which gives a possibility (if) and explains what may happen because of the possibility (then). The statement could also include "may," according to California State University, Bakersfield .

Here are some examples of hypothesis statements:

If garlic repels fleas, then a dog that is given garlic every day will not get fleas.
If sugar causes cavities, then people who eat a lot of candy may be more prone to cavities.
If ultraviolet light can damage the eyes, then maybe this light can cause blindness.

A useful hypothesis should be testable and falsifiable. That means that it should be possible to prove it wrong. A theory that can't be proved wrong is nonscientific, according to Karl Popper's 1963 book " Conjectures and Refutations ."

An example of an untestable statement is, "Dogs are better than cats." That's because the definition of "better" is vague and subjective. However, an untestable statement can be reworded to make it testable. For example, the previous statement could be changed to this: "Owning a dog is associated with higher levels of physical fitness than owning a cat." With this statement, the researcher can take measures of physical fitness from dog and cat owners and compare the two.

Types of scientific hypotheses

Elementary-age students study alternative energy using homemade windmills during public school science class.

In an experiment, researchers generally state their hypotheses in two ways. The null hypothesis predicts that there will be no relationship between the variables tested, or no difference between the experimental groups. The alternative hypothesis predicts the opposite: that there will be a difference between the experimental groups. This is usually the hypothesis scientists are most interested in, according to the University of Miami .

For example, a null hypothesis might state, "There will be no difference in the rate of muscle growth between people who take a protein supplement and people who don't." The alternative hypothesis would state, "There will be a difference in the rate of muscle growth between people who take a protein supplement and people who don't."

If the results of the experiment show a relationship between the variables, then the null hypothesis has been rejected in favor of the alternative hypothesis, according to the book " Research Methods in Psychology " (BCcampus, 2015).

There are other ways to describe an alternative hypothesis. The alternative hypothesis above does not specify a direction of the effect, only that there will be a difference between the two groups. That type of prediction is called a two-tailed hypothesis. If a hypothesis specifies a certain direction — for example, that people who take a protein supplement will gain more muscle than people who don't — it is called a one-tailed hypothesis, according to William M. K. Trochim , a professor of Policy Analysis and Management at Cornell University.

Sometimes, errors take place during an experiment. These errors can happen in one of two ways. A type I error is when the null hypothesis is rejected when it is true. This is also known as a false positive. A type II error occurs when the null hypothesis is not rejected when it is false. This is also known as a false negative, according to the University of California, Berkeley .

A hypothesis can be rejected or modified, but it can never be proved correct 100% of the time. For example, a scientist can form a hypothesis stating that if a certain type of tomato has a gene for red pigment, that type of tomato will be red. During research, the scientist then finds that each tomato of this type is red. Though the findings confirm the hypothesis, there may be a tomato of that type somewhere in the world that isn't red. Thus, the hypothesis is true, but it may not be true 100% of the time.

Scientific theory vs. scientific hypothesis

The best hypotheses are simple. They deal with a relatively narrow set of phenomena. But theories are broader; they generally combine multiple hypotheses into a general explanation for a wide range of phenomena, according to the University of California, Berkeley . For example, a hypothesis might state, "If animals adapt to suit their environments, then birds that live on islands with lots of seeds to eat will have differently shaped beaks than birds that live on islands with lots of insects to eat." After testing many hypotheses like these, Charles Darwin formulated an overarching theory: the theory of evolution by natural selection.

"Theories are the ways that we make sense of what we observe in the natural world," Tanner said. "Theories are structures of ideas that explain and interpret facts."

Read more about writing a hypothesis, from the American Medical Writers Association.
Find out why a hypothesis isn't always necessary in science, from The American Biology Teacher.
Learn about null and alternative hypotheses, from Prof. Essa on YouTube .

Encyclopedia Britannica. Scientific Hypothesis. Jan. 13, 2022. https://www.britannica.com/science/scientific-hypothesis

Karl Popper, "The Logic of Scientific Discovery," Routledge, 1959.

California State University, Bakersfield, "Formatting a testable hypothesis." https://www.csub.edu/~ddodenhoff/Bio100/Bio100sp04/formattingahypothesis.htm

Karl Popper, "Conjectures and Refutations," Routledge, 1963.

Price, P., Jhangiani, R., & Chiang, I., "Research Methods of Psychology — 2nd Canadian Edition," BCcampus, 2015.‌

University of Miami, "The Scientific Method" http://www.bio.miami.edu/dana/161/evolution/161app1_scimethod.pdf

William M.K. Trochim, "Research Methods Knowledge Base," https://conjointly.com/kb/hypotheses-explained/

University of California, Berkeley, "Multiple Hypothesis Testing and False Discovery Rate" https://www.stat.berkeley.edu/~hhuang/STAT141/Lecture-FDR.pdf

University of California, Berkeley, "Science at multiple levels" https://undsci.berkeley.edu/article/0_0_0/howscienceworks_19

Sign up for the Live Science daily newsletter now

Get the world’s most fascinating discoveries delivered straight to your inbox.

Y chromosome is evolving faster than the X, primate study reveals

Earth from space: Trio of ringed ice caps look otherworldly on Russian Arctic islands

Strawberry Moon 2024: See summer's first full moon rise a day after solstice

Most Popular

2 Mysterious 4,000-year-old 'palace' with maze-like walls found on Greek island of Crete
3 NASA will put a 'new star' in the sky by the end of the decade in 1st-of-its-kind mission
4 How long would it take to reach Planet 9, if we ever find it?
5 Planet Nine: Is the search for this elusive world nearly over?
2 Scientists inserted a window in a man's skull to read his brain with ultrasound
3 Gilgamesh flood tablet: A 2,600-year-old text that's eerily similar to the story of Noah's Ark
4 Y chromosome is evolving faster than the X, primate study reveals
5 The sun's magnetic field is about to flip. Here's what to expect.

IMAGES

Discover the Power of Controlled Experiments
Scientific Method Experiment Ideas
What Is a Controlled Experiment?
Example of a controlled experiment using the Scientific Method
PPT
Creating a Controlled Experiment

VIDEO

What is Randomized Controlled Trials (RCT)
What A Science Experiment! #experiment #science #shorts
Mimic Robot
Independent & dependent variables and controlled experiments
Controlled Set-up Definition and Example
What is the scientific method? Part One

COMMENTS

Controlled experiments (article)
The scientific method begins with an observation, which leads the scientist to ask a question. ... When possible, scientists test their hypotheses using controlled experiments. A controlled experiment is a scientific test done under controlled conditions, meaning that just one (or a few) factors are changed at a time, while all others are kept ...
What Is a Controlled Experiment?
Published on April 19, 2021 by Pritha Bhandari . Revised on June 22, 2023. In experiments, researchers manipulate independent variables to test their effects on dependent variables. In a controlled experiment, all variables other than the independent variable are controlled or held constant so they don't influence the dependent variable.
The scientific method (article)
The scientific method. At the core of biology and other sciences lies a problem-solving approach called the scientific method. The scientific method has five basic steps, plus one feedback step: Make an observation. Ask a question. Form a hypothesis, or testable explanation. Make a prediction based on the hypothesis.
What Is a Controlled Experiment?
Search. A controlled experiment aims to demonstrate causation between variables by manipulating an independent variable while controlling all other factors that could influence the results. Its purpose is to show that changes in one variable (the independent variable) directly cause changes in another variable (the dependent variable).
Controlled Experiments: Definition and Examples
In controlled experiments, researchers use random assignment (i.e. participants are randomly assigned to be in the experimental group or the control group) in order to minimize potential confounding variables in the study. For example, imagine a study of a new drug in which all of the female participants were assigned to the experimental group and all of the male participants were assigned to ...
What Is a Controlled Experiment?
Controlled Experiment. A controlled experiment is simply an experiment in which all factors are held constant except for one: the independent variable. A common type of controlled experiment compares a control group against an experimental group. All variables are identical between the two groups except for the factor being tested.
The scientific method (video)
The scientific method. The scientific method is a logical approach to understanding the world. It starts with an observation, followed by a question. A testable explanation or hypothesis is then created. An experiment is designed to test the hypothesis, and based on the results, the hypothesis is refined.
Controlled Experiments
Control in experiments is critical for internal validity, which allows you to establish a cause-and-effect relationship between variables. Example: Experiment. You're studying the effects of colours in advertising. You want to test whether using green for advertising fast food chains increases the value of their products.
Scientific control
A scientific control is an experiment or observation designed to minimize the effects of variables other than the independent variable ... Scientific controls are a part of the scientific method. Controlled experiments. Controls eliminate alternate explanations of experimental results, especially experimental errors and experimenter bias. ...
Why control an experiment?
The only way for systematically overcoming the limits of our sensory apparatus […] is through the Scientific Method, through hypothesis‐testing, controlled experimentation. Nominally, both positive and negative controls are material and procedural; that is, they control for variability of the experimental materials and the procedure itself.
Scientific method
The scientific method is an empirical method for acquiring knowledge that has characterized the development of science since at least the 17th century. ... while controlled experiments can be seen in the works of al-Battani (853-929 CE) and Alhazen (965-1039 CE). Communication and iteration Watson and Crick then produced their model, using ...
Controlled Experiment
Controlled Experiment Definition. A controlled experiment is a scientific test that is directly manipulated by a scientist, in order to test a single variable at a time. The variable being tested is the independent variable, and is adjusted to see the effects on the system being studied. The controlled variables are held constant to minimize or ...
A Guide to Using the Scientific Method in Everyday Life
Controlled experiments. The word "experiment" can be misleading because it implies a lack of control over the process. Therefore, it is important to understand that science uses controlled experiments in order to test hypotheses and contribute new knowledge. ... The scientific method has the merit of providing a reference system, with ...
The Scientific Method Tutorial
The Scientific Method Steps in the Scientific Method. There is a great deal of variation in the specific techniques scientists use explore the natural world. However, the following steps characterize the majority of scientific investigations: ... An experiment is a controlled series of observations designed to test a specific hypothesis. In an ...
PDF Evaluation Designing Controlled Experiments
Step 1: begin with a testable hypothesis. Step 2: explicitly state the independent variables. Step 3: carefully choose the dependent variables. step 4: consider possible nuisance variables & determine mitigation approach. Step 5: design the task to be performed. Step 6: design experiment protocol. Step 7: make formal experiment design explicit.
Control Group Definition and Examples
A control group is not the same thing as a control variable. A control variable or controlled variable is any factor that is held constant during an experiment. Examples of common control variables include temperature, duration, and sample size. The control variables are the same for both the control and experimental groups.
The scientific method and experimental design
The scientific method and experimental design. Google Classroom. Microsoft Teams. Which statement best describes a hypothesis? Choose 1 answer: The facts collected from an experiment are written in the form of a hypothesis. A.
Controlled Experiment
The five components of a controlled experiment are outlined in the scientific method: 1) defining the problem 2) making observations, 3) forming a hypothesis 4) conducting an experiment, and 5 ...
Experimental Method In Psychology
1. Lab Experiment. A laboratory experiment in psychology is a research method in which the experimenter manipulates one or more independent variables and measures the effects on the dependent variable under controlled conditions. A laboratory experiment is conducted under highly controlled conditions (not necessarily a laboratory) where ...
Definitions of Control, Constant, Independent and Dependent Variables
The scientific method includes three main types of variables: constants, independent, and dependent variables. ... and subsequent plant growth (dependent variable). The experiment should control the amount of water the plants receive and when, what type of soil they are planted in, the type of plant, and as many other different variables as ...
What Is a Control Variable? Definition and Examples
Control Variable Examples. Anything you can measure or control that is not the independent variable or dependent variable has potential to be a control variable. Examples of common control variables include: Duration of the experiment. Size and composition of containers. Temperature.
Biology and the scientific method review
Meaning. Biology. The study of living things. Observation. Noticing and describing events in an orderly way. Hypothesis. A scientific explanation that can be tested through experimentation or observation. Controlled experiment. An experiment in which only one variable is changed.
What Are the Scientific Method Steps?
The scientific method is like a structured adventure for exploring the world that encourages discovery by finding answers and solving puzzles. With the scientific method steps, students get to ask questions, observe, make educated guesses (called hypotheses), run experiments, collect and organize data, draw sensible conclusions, and share what ...
Controlled Experiments: Methods, Examples & Limitations
Research. Controlled Experiments: Methods, Examples & Limitations. What happens in experimental research is that the researcher alters the independent variables so as to determine their impacts on the dependent variables. Therefore, when the experiment is controlled, you can expect that the researcher will control all other variables except for ...
What is a scientific hypothesis?
A scientific hypothesis is a tentative, testable explanation for a phenomenon in the natural world. It's the initial building block in the scientific method.Many describe it as an "educated guess ...

Have a language expert improve your writing

What Is a Controlled Experiment? | Definitions & Examples

Table of contents

Prevent plagiarism. Run a free check.

Control groups

Random assignment

Masking (blinding)

Difficult to control all variables

Risk of low external validity

Here's why students love Scribbr's proofreading services

Cite this Scribbr article

Is this article helpful?

Pritha Bhandari

What Is a Controlled Experiment?

Controlled Experiment

Example of a Controlled Experiment

Why Controlled Experiments Are Important

Are All Experiments Controlled?

Have a language expert improve your writing

Controlled Experiments | Methods & Examples of Control

Table of contents

Prevent plagiarism, run a free check.

Control groups

Random assignment

Masking (blinding)

Difficult to control all variables

Risk of low external validity

Cite this Scribbr article

Is this article helpful?

Pritha Bhandari

Why control an experiment?

František Baluška

P < 0.05 tacitly acknowledges the explicate order

Further reading

Acknowledgements

Contributor Information

Controlled Experiment

Controlled Experiment Definition

Examples of Controlled Experiment

Scurvy in Sailors

Related Biology Terms

Cite This Article

The Scientific Method Tutorial

The Scientific Method

The Scientific Method Flowchart

The Scientific Method in Detail

Step 1: Observations

Limits of Observations

Step 2: The Hypothesis

The Qualities of a Good Hypothesis

Step 3: Testing the Hypothesis

Testing a Hypothesis by Observation

Testing a Hypothesis by Experimentation

The Qualities of a Good Experiment

Variation among Subjects

Measurement Error

Step 4: Data Analysis

Step 5: Stating Conclusions

Qualities of a Good Experiment

Experimental Method In Psychology

What is an Experiment?

1. Lab Experiment

2. Field Experiment

3. Natural Experiment

Key Terminology

Experimenter effects

Demand characteristics

Independent variable (IV)

Dependent variable (DV)

Extraneous variables (EV)

Confounding variables

Random Allocation

Order effects

Sciencing_Icons_Science SCIENCE

Definitions of Control, Constant, Independent and Dependent Variables in a Science Experiment

Why Should You Only Test for One Variable at a Time in an Experiment?

Constant Variables

Independent Variable

Dependent Variable

Control Groups