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What Is a Constant in the Scientific Method?

Can a Science Experiment Have Two Manipulated Variables?

The scientific method forms the foundation of the collective knowledge of the world around us. It is how researchers figure out what is likely true in nature. A scientific method experiment begins with a hypothesis, which is an informed opinion that explains why certain things occur the way they do. In science, hypotheses lead to predictions. These are measurable events that occur during an experiment if the hypothesis is true. The most significant components of the scientific method include hypotheses, dependent and independent variables, constant variables and control groups.

TL;DR (Too Long; Didn't Read)

A constant variable is an aspect of an experiment that a scientist or researcher keeps unchanged. There can be more than one constant in an experiment.

Through rigorous experimentation and corroboration, which requires other scientists to duplicate the same result as the first, a scientist's hypothesis is either confirmed or proven incorrect. While many people think of only men and women in white lab coats using the scientific method, it is an intuitive process. If you've ever asked yourself whether something is true or why something is the way it is—why is the sky blue?—you've performed the first step of the scientific method.

Why the Scientific Method Is Important

There is a good reason teachers introduce the scientific method early in a science class. It's the most important fundamental tool of science. Without the scientific method, there would be no way for scientists to agree on what is likely true and what is not.

The term "science" comes from the Latin word for "knowing." The scientific method is the process used to know that a new idea is valid. The confirmation of these new ideas has both theoretical and practical implications. For example, they can increase our knowledge of the universe and how it works. New ideas can lead to the development of inventions that change how people live.

There are three types of variables used in scientific experiments: constant, independent and dependent.

A constant variable is any aspect of an experiment that a researcher intentionally keeps unchanged throughout an experiment.

Experiments are always testing for measurable change, which is the dependent variable. You can also think of a dependent variable as the result obtained from an experiment. It is dependent on the change that occurs. Scientists introduce an independent variable to an experiment to create a change in the dependent variable. There can only be one independent variable in each experiment, but there will normally be many constant variables.

To illustrate a constant variable by looking at an example, let's say a new drug comes out that claims to make it easier to lose weight. Each scientific experiment can only focus on one independent variable or make one change at a time. If researchers gave a group of people this new drug and also increased the amount of exercise each person in the study did, it would complicate the picture. Scientists wouldn't be able to tell whether the pill or the exercise was responsible for any changes in weight, the dependent variable.

To ensure that only one independent variable exists, everything else is held constant. So, the constant variables in this experiment investigating the effects of the diet pill would be variables like the number of calories consumed by each participant, the amount of exercise they get, how much sleep they receive, etc. The constants are all the other aspects that are held the same for each participant.

Difference Between a Control and a Constant

You may think that a constant is the same thing as a control, but there is a difference. A control is specifically set aside without any changes to give the researcher an objective picture of any changes in the independent variable. For studies of drugs, a placebo is the control. A person is not told whether they're taking a diet pill or a placebo. A control negates the possible effects of persons believing they are taking diet pills when they are not.

When using the experimental method, it is critical to understand which variables are constants and which are controls. This helps to ensure any changes to the dependent variable are a result of the independent variable alone.

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About the Author

Amanda Cook holds a Bachelor of Science in Microbiology and a Doctorate in Health and Human Performance from Middle Tennessee State University. She has been writing online professionally since 2009.

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Understanding Constants In An Experiment

• December 11, 2020
• Science Facts

Constants In An Experiment

The term ‘constant’ is used to refer to a particular quantity which is not intended to alter or change.

What are Constants?

Amongst the other fields, constants are also used in during various situations of an experiment. Such constants which are implemented in experiments are referred to as experimental constants.

Like other constants, experimental constants are also measurable. However, they cannot be changed in the due course of the experiment or in-between them.

There are various values which are considered as constants during experiments.

Constituents of natural forces such as the speed of light or the atomic weight of gold are considered as experimental constants.

Additionally, there are various properties which are considered to be experimental constants. The prime example of this is the boiling point of water.

The boiling point of water depends may alter depending on the altitude and the decrease in acceleration due to gravity.

However, experiments involving water in a single location consider its boiling point as constants.

The Need for Constants

There are various reasons why we need to implement constants within our experiments. However, they all stem out from the same characteristic, that is duplication of results or consistency in results.

Whenever we perform any experiment, we do so carefully ensuring that the process can be duplicated again as required.

If we involved a plethora of variables it would that we would receive a ton of variable results as well. This would completely defeat the purpose of experimenting.

Factors Considered as Constant in an Experiment

When we are on a lookout for constants, we essentially look for such factors which are considered to be similar in all states or conditions.

Irrespective of the time or the nature of this aforementioned factor, it will never change its state.

This stems out from the fact that a constant never changes its state in the duration of an experiment.

Understanding a constant becomes easier when one contrasts the constant factor with a mathematical constant.

In the field of mathematics, a constant refers to a particular factor which has a fixed numerical value.

In the same way, a constant in an experiment does not change its state and is universally equal all-around.

The only situation in which a mathematical constant and an experimental constant differ is that a mathematical constant does not involve any physical measurement.

Examples of Constants in Experiments

When you consider the factors used for determining an experimental constant, there are various constants that you might come across.

A few good examples of experimental constants include:

• The acceleration due to gravity
• Gravitational constant
• Avogadro’s constant
• The Gas constant
• Boltzmann’s constant
• The Stefan-Boltzmann constant
• Elementary charge
• Electron rest mass
• Proton rest mass
• Unified atomic mass unit
• Solar constant, and much more.

Apart from these constants, there are various other factors which are also considered as constants like Planck’s constant, the permittivity of free space, etc.

In essence, if you want to determine whether or not a particular factor is considered as a constant, you might want to think about such factors which contain a form of measurement that is universal.  

Constants In A Scientific Method

The term scientific method refers to an approach seeking a particular form of knowledge involving the formulation of a hypothesis or testing for proving its validity.

More often than not, scientific methods require intense experimentation for proving their validity. And such experiments often involve constants.

You might ask the purpose of constants in scientific methods. Here’s why we need them.

Whenever you need to perform experiments, you need to test through various factors which often involve a lot of measurable change.

These changes occur due to the presence of the dependent variable. As a result, the changes occurring allude to the dependent variable.

To understand such changes, the experimenters often introduce an independent variable for creating changes in the dependent variables.

However, there should always be only a single independent variable in such experiments.

Even other factors such as the presence of other variables are included in the form of controlled variables. And this is known as a constant in a scientific method.

Controls or Controlled Variables

Now, when you encounter the term ‘variable’, you might refer to such factors which are constantly found to be changing during an experiment. And you are right as well.

For the definition of a variable, states that any factor, trait, or condition which is found to exist in different amounts or types is a variable.

As a result, it would be correct to not refer to them as constants, right? Well, not exactly.

There are various situations in which variables are considered as constants. There are certain experiments in which a person performing the experiments considers certain variables in a constant state.

Such variables are referred to as controlled variables. As a result of this, the experimenter can attain more clarity in isolating the relationship between the independent variables and the dependent variables.

Moreover, by considering certain variables as constants, experimenters are also able to achieve constants results whenever they experiment.

The Prime Example of Controlled Variables

In the due course of an experiment, there are various variables which an experimenter considers to be constant.

The prime example of such controlled variables is the boiling point of water.

As aforementioned in the introductory paragraph, the boiling point of water variably changes when factors such as altitude and the acceleration due to gravity are involved in the experiment.

In spite of this, the temperature is considered constant as it allows the experimenter to derive constant results for a particular location or space.

Other Examples of Controlled Variables

Apart from the boiling point of water, there are various other examples which beautifully explain the concept of controlled variables within experiments.

A few of these include:

• The amount of fertilizer which a plant uses for the crop outgrowth.
• The type of soil is used for planting a particular type of plant.
• The amount of time which is spent by children in trying to learn a new concept.
• The amount of sunlight which a plant uses for its growth.

In spite of these examples, there are cases in which you might feel confused regarding controlled variables.

In simple terms, a controlled variable is referred to as determiners which greatly influence the result.

Usually, experimenters are more focused on understanding whether or not control variables have any significant effects on results.

With the help of control variables, they can do the same while achieving the desired results or outcomes in a particular experiment.

The Control Group

The term ‘control group’ essentially refers to a particular standard used for making comparisons in a particular experiment.

Whenever an experimenter stages an experiment, he or she designs it, particularly to include a control group and one or more experimental groups.

In an ideal sense, both the experimental groups and the control groups are similar.

However, the dissimilarity arises between these groups when the experimental group is subjected to various treatments which are believed to affect the outcome of the treatment.

Conversely, the control group isn’t subjected to any form of treatment or intervention which could affect the outcomes arising from the treatment.  

The Need For A Control Group

When you think about the involvement of a control group within the confinements of an experiment, you might think about the need which gives birth to such a need.

The primary reason why we need a control group is for the experimenters to easily be able to conclude a particular study.

It is only with the help of a control group that experimenters can determine whether or not a particular experiment can have a significant effect on the experimental group that can be recorded.

Moreover, the inclusion of a constant group within an experiment also ensures that the possibilities of making errors during the derivation of results are vastly minimized.

The Differences Between Constants and Controls

People often get confused with the different concepts that are involved with constants and controlled variables.

This is not only because they both begin with the same alphabet and that they sound the same, but it is also due to the similarity in their definitions.

However, you can be assured that these concepts don’t define the same things.

When we talk about constants, we essentially talk about the factors that are non-varying.

These factors are universally fixed and defined so that they are unable to start any changes that occur at the time of the results.

However, the purpose of control or a controlled variable isn’t the same.

Unlike a constant, a control or controlled variable is set aside to ignore the occurrence of any changes in the result that rise from the independent variable.

This ensures that the experimenter can view the experiment from an objective point of view.

When experimenters implement an experimental method, they do so understandingly which variables are controls and which of them are constants.

It is only by differentiating the controls and the constants that they can understand the changes occurring in the dependent variable.

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Biology archive

Course: biology archive   >   unit 1.

• The scientific method

Controlled experiments

• The scientific method and experimental design

Introduction

How are hypotheses tested.

• One pot of seeds gets watered every afternoon.
• The other pot of seeds doesn't get any water at all.

Control and experimental groups

Independent and dependent variables, independent variables, dependent variables, variability and repetition, controlled experiment case study: co 2 ‍   and coral bleaching.

• What your control and experimental groups would be
• What your independent and dependent variables would be
• What results you would predict in each group

Experimental setup

• Some corals were grown in tanks of normal seawater, which is not very acidic ( pH ‍   around 8.2 ‍   ). The corals in these tanks served as the control group .
• Other corals were grown in tanks of seawater that were more acidic than usual due to addition of CO 2 ‍   . One set of tanks was medium-acidity ( pH ‍   about 7.9 ‍   ), while another set was high-acidity ( pH ‍   about 7.65 ‍   ). Both the medium-acidity and high-acidity groups were experimental groups .
• In this experiment, the independent variable was the acidity ( pH ‍   ) of the seawater. The dependent variable was the degree of bleaching of the corals.
• The researchers used a large sample size and repeated their experiment. Each tank held 5 ‍   fragments of coral, and there were 5 ‍   identical tanks for each group (control, medium-acidity, and high-acidity). Note: None of these tanks was "acidic" on an absolute scale. That is, the pH ‍   values were all above the neutral pH ‍   of 7.0 ‍   . However, the two groups of experimental tanks were moderately and highly acidic to the corals , that is, relative to their natural habitat of plain seawater.

Analyzing the results

Non-experimental hypothesis tests, case study: coral bleaching and temperature, attribution:, works cited:.

• Hoegh-Guldberg, O. (1999). Climate change, coral bleaching, and the future of the world's coral reefs. Mar. Freshwater Res. , 50 , 839-866. Retrieved from www.reef.edu.au/climate/Hoegh-Guldberg%201999.pdf.
• Anthony, K. R. N., Kline, D. I., Diaz-Pulido, G., Dove, S., and Hoegh-Guldberg, O. (2008). Ocean acidification causes bleaching and productivity loss in coral reef builders. PNAS , 105 (45), 17442-17446. http://dx.doi.org/10.1073/pnas.0804478105 .
• University of California Museum of Paleontology. (2016). Misconceptions about science. In Understanding science . Retrieved from http://undsci.berkeley.edu/teaching/misconceptions.php .
• Hoegh-Guldberg, O. and Smith, G. J. (1989). The effect of sudden changes in temperature, light and salinity on the density and export of zooxanthellae from the reef corals Stylophora pistillata (Esper, 1797) and Seriatopora hystrix (Dana, 1846). J. Exp. Mar. Biol. Ecol. , 129 , 279-303. Retrieved from http://www.reef.edu.au/ohg/res-pic/HG%20papers/HG%20and%20Smith%201989%20BLEACH.pdf .

Additional references:

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What Does ‘Constant’ Mean In Science?

Constants play a vital role across scientific disciplines by representing fixed, unchanging values. The fine structure constant in physics or the speed of light are examples of universal constants that remain the same under all conditions.

If you’re short on time, here’s a quick answer to your question: In science, a constant is a fixed, unchanging value or quantity that remains the same under specific conditions for a particular experiment or scenario .

In this detailed guide, we will explore the meaning of constants in science, provide examples from different fields, explain why they are important, and look at some well-known scientific constants used in equations and experiments.

Definition and Explanation of a Scientific Constant

In the realm of science, a constant refers to a value that remains unchanging and fixed. It is a fundamental concept that provides stability and reliability to scientific theories, experiments, and calculations.

Constants play a crucial role in the development of scientific laws, formulas, and calculations, as they allow scientists to make accurate predictions and draw meaningful conclusions.

Unchanging, fixed value

A scientific constant is a value that does not vary under normal circumstances. It remains consistent regardless of the conditions or variables involved. This stability allows scientists to establish a reliable baseline against which other measurements and observations can be compared.

For example, the speed of light in a vacuum, denoted by the symbol ‘c’, is considered a constant because it remains the same value, approximately 299,792,458 meters per second, regardless of the observer’s frame of reference.

Applies under defined conditions

Scientific constants apply within specific conditions or contexts. They are valid within a defined range of parameters, such as temperature, pressure, or composition. For instance, Avogadro’s constant, represented by ‘NA’, is a fundamental constant in chemistry that relates the number of atoms or molecules in a given amount of substance.

It is approximately equal to 6.022 x 10^23 particles per mole and is applicable in calculations involving gases and chemical reactions at standard temperature and pressure.

Used in scientific laws, formulas, and calculations

Scientific constants are utilized in the formulation of laws, formulas, and calculations in various scientific disciplines. They serve as building blocks for understanding and predicting natural phenomena.

For example, in physics, the gravitational constant ‘G’ is used in Newton’s law of universal gravitation to calculate the force of attraction between two objects. This constant allows scientists to accurately predict the motion of celestial bodies and explain phenomena such as planetary orbits and gravitational interactions.

It is important to note that scientific constants are not arbitrary values chosen by scientists. They are determined through extensive experimentation, observation, and mathematical analysis. These constants are often based on years, if not centuries, of research and are accepted within the scientific community as reliable and accurate representations of the natural world.

Examples of Famous Scientific Constants

In science, a constant is a value that does not change under specific conditions. Constants play a crucial role in various scientific theories and equations, providing a foundation for understanding the natural world. Here are some examples of famous scientific constants:

Speed of light (c)

The speed of light, denoted by the symbol ‘c’, is a fundamental constant in physics. It represents the maximum speed at which information or energy can travel in the universe. According to the current scientific understanding, the speed of light in a vacuum is approximately 299,792,458 meters per second.

Gravitational constant (G)

The gravitational constant, denoted by the symbol ‘G’, is a fundamental constant that characterizes the strength of the gravitational force between two objects. It plays a crucial role in Isaac Newton’s law of universal gravitation and Albert Einstein’s theory of general relativity.

The value of the gravitational constant is approximately 6.67430 x 10^-11 N(m/kg)^2.

Planck’s constant (h)

Planck’s constant, symbolized by ‘h’, is a fundamental constant in quantum mechanics. It relates the energy of a photon to its frequency and is used to calculate the energy levels of particles at the atomic and subatomic scale.

The value of Planck’s constant is approximately 6.62607015 x 10^-34 joule-seconds.

Gas constant (R)

The gas constant, denoted by the symbol ‘R’, is a universal constant that relates the properties of an ideal gas. It appears in the ideal gas law equation, which describes the behavior of gases under various conditions.

The value of the gas constant depends on the units used and is approximately 8.314 J/(mol·K).

Avogadro’s number

Avogadro’s number, denoted by ‘N A ‘, is a constant that represents the number of atoms or molecules in one mole of a substance. It is an essential constant in chemistry and is used to convert between the mass of a substance and the number of particles it contains.

The value of Avogadro’s number is approximately 6.02214076 x 10^23.

Universal constants in physics

Besides the examples mentioned above, there are several other universal constants in physics, such as the Boltzmann constant (k), the elementary charge (e), and the magnetic constant (μ 0 ). These constants have significant roles in various scientific disciplines and are used in a wide range of calculations and equations.

Mathematical constants like pi

In addition to the physical constants, there are also mathematical constants that hold a special place in science. One of the most famous mathematical constants is pi (π), which represents the ratio of the circumference of a circle to its diameter.

Pi is an irrational number, approximately equal to 3.14159, and it appears in numerous mathematical formulas and calculations.

These examples of famous scientific constants demonstrate the importance of constants in scientific research. They provide a solid foundation for understanding the laws of nature and enable scientists to make accurate predictions and calculations.

By using these constants, scientists can unlock the mysteries of the universe and push the boundaries of human knowledge.

Importance and Use of Scientific Constants

In the world of science, constants play a crucial role in understanding and explaining natural phenomena. These values remain fixed and unchanged, allowing scientists to make accurate calculations and draw meaningful conclusions.

Here are some key reasons why scientific constants are of utmost importance:

Allow precise quantitative analysis

Scientific constants provide a foundation for precise quantitative analysis. By having a known and consistent value, scientists can make accurate measurements and calculations in their experiments. This precision allows for more reliable and meaningful results.

For example, the speed of light, a fundamental constant in physics, enables scientists to calculate distances in space and time with great accuracy.

Enable the creation of scientific laws and formulas

Scientific constants are essential in the creation of scientific laws and formulas. These laws describe the relationships between different variables and constants, allowing scientists to make predictions and understand the underlying principles of nature.

For instance, Newton’s law of universal gravitation, which includes the gravitational constant, G, allows scientists to predict the gravitational force between two objects.

Support reproducibility in experiments

Reproducibility is a cornerstone of scientific research. Scientific constants contribute to the reproducibility of experiments by providing fixed reference points. With known constants, scientists can compare their results with those obtained by other researchers, ensuring that their findings are consistent and reliable.

This promotes transparency and helps to build upon previous knowledge. A well-known example is Avogadro’s constant, which allows scientists to determine the number of atoms or molecules in a given sample.

Provide fixed reference points

Scientific constants provide fixed reference points against which other measurements can be made. They serve as benchmarks that allow scientists to establish standardized units of measurement. For instance, the Planck constant, h, is a fundamental constant in quantum mechanics that defines the relationship between energy and frequency.

Its precise value provides a reference point for measuring the energy of particles and electromagnetic waves.

Allow measurement of other variables

Scientific constants enable the measurement of other variables that may be difficult to quantify directly. By incorporating known constants into equations, scientists can indirectly determine the values of various quantities.

This indirect measurement technique is often employed in fields like astrophysics and quantum mechanics. For example, the Boltzmann constant, k, allows scientists to determine the average kinetic energy of particles in a gas based on temperature.

Constants vs. Variables in Science Experiments

Constants remain fixed, variables can change.

In scientific experiments, constants and variables play crucial roles. A constant is a factor that remains unchanged throughout the entire experiment, while a variable is a factor that can be manipulated or changed.

Constants are carefully chosen to provide a stable and consistent baseline for comparison.

For example, imagine conducting an experiment to test the effect of temperature on the rate of a chemical reaction. In this case, the amount of reactants, the pressure, and the concentration of the solution would all be considered constants.

These variables are kept constant to ensure that any changes observed in the reaction rate can be attributed solely to the temperature.

Constants provide controls in experiments

Constants serve as controls in scientific experiments. By keeping certain factors constant, researchers can isolate the effects of the variable they are studying. This allows them to accurately determine the relationship between the variable and the observed results.

Continuing with the example of the temperature experiment, if the concentration of the solution were not kept constant, it could introduce confounding variables that would cloud the results. By controlling the concentration, researchers can confidently attribute any changes in the reaction rate solely to the temperature.

Changes in variables are measured against constants

Changes in variables are measured and compared against the constants in a scientific experiment. This comparison allows researchers to draw meaningful conclusions about the relationship between the variable and the outcomes.

Returning to the temperature experiment, the reaction rate at different temperatures would be measured and compared against the constant factors such as the concentration and pressure. By analyzing the data, researchers can determine how changes in temperature affect the rate of the chemical reaction.

Understanding the distinction between constants and variables is essential in scientific research. It ensures that experiments are conducted with precision and accuracy, allowing for reliable and reproducible results.

Establishing and Measuring Scientific Constants

In the field of science, constants play a crucial role in understanding and explaining the natural world. These constants are values that remain unchanged under specific conditions and are used as a foundation for scientific research and experimentation.

One such important aspect of establishing and measuring scientific constants is through high precision empirical measurements.

High precision empirical measurements

Scientists use advanced instruments and techniques to make precise measurements of physical quantities. These measurements are based on empirical evidence obtained through rigorous experimentation and observation.

By repeatedly measuring a specific phenomenon, scientists can establish the value of a constant with a high degree of accuracy. For example, the speed of light, denoted by the symbol ‘c,’ is a well-known constant that has been determined through meticulous measurements and experiments.

Consensus of scientific community

Scientific constants are not established arbitrarily; they require a consensus among the scientific community. When multiple researchers and scientists independently obtain similar results for a specific constant, it strengthens the reliability and validity of that value.

This consensus is crucial in ensuring the accuracy and credibility of scientific constants. It also allows scientists to build upon existing knowledge and theories, leading to further advancements in their respective fields.

Technological advances enabling greater accuracy

Technological advancements have played a significant role in enabling scientists to measure constants with greater precision. Improved instruments, such as atomic clocks and particle detectors, have revolutionized the way scientists can measure and understand the physical world.

These technological advancements have allowed for the refinement of existing constants and the discovery of new ones. For instance, advancements in quantum mechanics have led to the establishment of the Planck constant, which plays a fundamental role in understanding the behavior of subatomic particles.

Importance of exact, standardized values

Exact and standardized values of constants are of paramount importance in scientific research and development. They provide a common language for scientists to communicate and collaborate effectively. Standardized values allow for consistency and reproducibility in experiments, ensuring that results obtained by different researchers are comparable.

This is essential for advancing scientific knowledge and developing practical applications. For example, the value of the gravitational constant, denoted by ‘G’, is crucial in various fields, including astrophysics and engineering, where accurate calculations and predictions are required.

Establishing and measuring scientific constants is a meticulous process that combines empirical evidence, consensus among the scientific community, and technological advancements. These constants serve as the building blocks of scientific knowledge, enabling researchers to understand the natural world and make significant advancements in various fields.

They provide a solid foundation for scientific theories and practical applications, contributing to the progress of society as a whole.

Constants serve an important role in science by representing fixed, known values that remain unchanged under defined conditions. They enable quantitative analysis, precise formulas and reproducible experiments.

Famous constants like the speed of light, gravitational constant and Planck’s constant are fundamental to physics and other areas of science. Understanding what constants represent and how they are established, measured and used is key for scientific learning.

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Experimental Design - Independent, Dependent, and Controlled Variables

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Scientific experiments are meant to show cause and effect of a phenomena (relationships in nature).  The “ variables ” are any factor, trait, or condition that can be changed in the experiment and that can have an effect on the outcome of the experiment.

An experiment can have three kinds of variables: i ndependent, dependent, and controlled .

• The independent variable is one single factor that is changed by the scientist followed by observation to watch for changes. It is important that there is just one independent variable, so that results are not confusing.
• The dependent variable is the factor that changes as a result of the change to the independent variable.
• The controlled variables (or constant variables) are factors that the scientist wants to remain constant if the experiment is to show accurate results. To be able to measure results, each of the variables must be able to be measured.

For example, let’s design an experiment with two plants sitting in the sun side by side. The controlled variables (or constants) are that at the beginning of the experiment, the plants are the same size, get the same amount of sunlight, experience the same ambient temperature and are in the same amount and consistency of soil (the weight of the soil and container should be measured before the plants are added). The independent variable is that one plant is getting watered (1 cup of water) every day and one plant is getting watered (1 cup of water) once a week. The dependent variables are the changes in the two plants that the scientist observes over time.

Can you describe the dependent variable that may result from this experiment? After four weeks, the dependent variable may be that one plant is taller, heavier and more developed than the other. These results can be recorded and graphed by measuring and comparing both plants’ height, weight (removing the weight of the soil and container recorded beforehand) and a comparison of observable foliage.

Using What You Learned: Design another experiment using the two plants, but change the independent variable. Can you describe the dependent variable that may result from this new experiment?

Think of another simple experiment and name the independent, dependent, and controlled variables. Use the graphic organizer included in the PDF below to organize your experiment's variables.

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Independent and Dependent Variables: Which Is Which?

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Independent and dependent variables are important for both math and science. If you don't understand what these two variables are and how they differ, you'll struggle to analyze an experiment or plot equations. Fortunately, we make learning these concepts easy!

In this guide, we break down what independent and dependent variables are , give examples of the variables in actual experiments, explain how to properly graph them, provide a quiz to test your skills, and discuss the one other important variable you need to know.

What Is an Independent Variable? What Is a Dependent Variable?

A variable is something you're trying to measure. It can be practically anything, such as objects, amounts of time, feelings, events, or ideas. If you're studying how people feel about different television shows, the variables in that experiment are television shows and feelings. If you're studying how different types of fertilizer affect how tall plants grow, the variables are type of fertilizer and plant height.

There are two key variables in every experiment: the independent variable and the dependent variable.

Independent variable: What the scientist changes or what changes on its own.

Dependent variable: What is being studied/measured.

The independent variable (sometimes known as the manipulated variable) is the variable whose change isn't affected by any other variable in the experiment. Either the scientist has to change the independent variable herself or it changes on its own; nothing else in the experiment affects or changes it. Two examples of common independent variables are age and time. There's nothing you or anything else can do to speed up or slow down time or increase or decrease age. They're independent of everything else.

The dependent variable (sometimes known as the responding variable) is what is being studied and measured in the experiment. It's what changes as a result of the changes to the independent variable. An example of a dependent variable is how tall you are at different ages. The dependent variable (height) depends on the independent variable (age).

An easy way to think of independent and dependent variables is, when you're conducting an experiment, the independent variable is what you change, and the dependent variable is what changes because of that. You can also think of the independent variable as the cause and the dependent variable as the effect.

It can be a lot easier to understand the differences between these two variables with examples, so let's look at some sample experiments below.

Examples of Independent and Dependent Variables in Experiments

Below are overviews of three experiments, each with their independent and dependent variables identified.

Experiment 1: You want to figure out which brand of microwave popcorn pops the most kernels so you can get the most value for your money. You test different brands of popcorn to see which bag pops the most popcorn kernels.

• Independent Variable: Brand of popcorn bag (It's the independent variable because you are actually deciding the popcorn bag brands)
• Dependent Variable: Number of kernels popped (This is the dependent variable because it's what you measure for each popcorn brand)

Experiment 2 : You want to see which type of fertilizer helps plants grow fastest, so you add a different brand of fertilizer to each plant and see how tall they grow.

• Independent Variable: Type of fertilizer given to the plant
• Dependent Variable: Plant height

Experiment 3: You're interested in how rising sea temperatures impact algae life, so you design an experiment that measures the number of algae in a sample of water taken from a specific ocean site under varying temperatures.

• Independent Variable: Ocean temperature
• Dependent Variable: The number of algae in the sample

For each of the independent variables above, it's clear that they can't be changed by other variables in the experiment. You have to be the one to change the popcorn and fertilizer brands in Experiments 1 and 2, and the ocean temperature in Experiment 3 cannot be significantly changed by other factors. Changes to each of these independent variables cause the dependent variables to change in the experiments.

Where Do You Put Independent and Dependent Variables on Graphs?

Independent and dependent variables always go on the same places in a graph. This makes it easy for you to quickly see which variable is independent and which is dependent when looking at a graph or chart. The independent variable always goes on the x-axis, or the horizontal axis. The dependent variable goes on the y-axis, or vertical axis.

Here's an example:

As you can see, this is a graph showing how the number of hours a student studies affects the score she got on an exam. From the graph, it looks like studying up to six hours helped her raise her score, but as she studied more than that her score dropped slightly.

The amount of time studied is the independent variable, because it's what she changed, so it's on the x-axis. The score she got on the exam is the dependent variable, because it's what changed as a result of the independent variable, and it's on the y-axis. It's common to put the units in parentheses next to the axis titles, which this graph does.

There are different ways to title a graph, but a common way is "[Independent Variable] vs. [Dependent Variable]" like this graph. Using a standard title like that also makes it easy for others to see what your independent and dependent variables are.

Are There Other Important Variables to Know?

Independent and dependent variables are the two most important variables to know and understand when conducting or studying an experiment, but there is one other type of variable that you should be aware of: constant variables.

Constant variables (also known as "constants") are simple to understand: they're what stay the same during the experiment. Most experiments usually only have one independent variable and one dependent variable, but they will all have multiple constant variables.

For example, in Experiment 2 above, some of the constant variables would be the type of plant being grown, the amount of fertilizer each plant is given, the amount of water each plant is given, when each plant is given fertilizer and water, the amount of sunlight the plants receive, the size of the container each plant is grown in, and more. The scientist is changing the type of fertilizer each plant gets which in turn changes how much each plant grows, but every other part of the experiment stays the same.

In experiments, you have to test one independent variable at a time in order to accurately understand how it impacts the dependent variable. Constant variables are important because they ensure that the dependent variable is changing because, and only because, of the independent variable so you can accurately measure the relationship between the dependent and independent variables.

If you didn't have any constant variables, you wouldn't be able to tell if the independent variable was what was really affecting the dependent variable. For example, in the example above, if there were no constants and you used different amounts of water, different types of plants, different amounts of fertilizer and put the plants in windows that got different amounts of sun, you wouldn't be able to say how fertilizer type affected plant growth because there would be so many other factors potentially affecting how the plants grew.

3 Experiments to Help You Understand Independent and Dependent Variables

If you're still having a hard time understanding the relationship between independent and dependent variable, it might help to see them in action. Here are three experiments you can try at home.

Experiment 1: Plant Growth Rates

One simple way to explore independent and dependent variables is to construct a biology experiment with seeds. Try growing some sunflowers and see how different factors affect their growth. For example, say you have ten sunflower seedlings, and you decide to give each a different amount of water each day to see if that affects their growth. The independent variable here would be the amount of water you give the plants, and the dependent variable is how tall the sunflowers grow.

Experiment 2: Chemical Reactions

Explore a wide range of chemical reactions with this chemistry kit . It includes 100+ ideas for experiments—pick one that interests you and analyze what the different variables are in the experiment!

Experiment 3: Simple Machines

Build and test a range of simple and complex machines with this K'nex kit . How does increasing a vehicle's mass affect its velocity? Can you lift more with a fixed or movable pulley? Remember, the independent variable is what you control/change, and the dependent variable is what changes because of that.

Quiz: Test Your Variable Knowledge

Can you identify the independent and dependent variables for each of the four scenarios below? The answers are at the bottom of the guide for you to check your work.

Scenario 1: You buy your dog multiple brands of food to see which one is her favorite.

Scenario 2: Your friends invite you to a party, and you decide to attend, but you're worried that staying out too long will affect how well you do on your geometry test tomorrow morning.

Scenario 3: Your dentist appointment will take 30 minutes from start to finish, but that doesn't include waiting in the lounge before you're called in. The total amount of time you spend in the dentist's office is the amount of time you wait before your appointment, plus the 30 minutes of the actual appointment

Scenario 4: You regularly babysit your little cousin who always throws a tantrum when he's asked to eat his vegetables. Over the course of the week, you ask him to eat vegetables four times.

Summary: Independent vs Dependent Variable

Knowing the independent variable definition and dependent variable definition is key to understanding how experiments work. The independent variable is what you change, and the dependent variable is what changes as a result of that. You can also think of the independent variable as the cause and the dependent variable as the effect.

When graphing these variables, the independent variable should go on the x-axis (the horizontal axis), and the dependent variable goes on the y-axis (vertical axis).

Constant variables are also important to understand. They are what stay the same throughout the experiment so you can accurately measure the impact of the independent variable on the dependent variable.

What's Next?

Independent and dependent variables are commonly taught in high school science classes. Read our guide to learn which science classes high school students should be taking.

Scoring well on standardized tests is an important part of having a strong college application. Check out our guides on the best study tips for the SAT and ACT.

Interested in science? Science Olympiad is a great extracurricular to include on your college applications, and it can help you win big scholarships. Check out our complete guide to winning Science Olympiad competitions.

Quiz Answers

1: Independent: dog food brands; Dependent: how much you dog eats

2: Independent: how long you spend at the party; Dependent: your exam score

3: Independent: Amount of time you spend waiting; Dependent: Total time you're at the dentist (the 30 minutes of appointment time is the constant)

4: Independent: Number of times your cousin is asked to eat vegetables; Dependent: number of tantrums

These recommendations are based solely on our knowledge and experience. If you purchase an item through one of our links, PrepScholar may receive a commission.

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Home » Control Variable – Definition, Types and Examples

Control Variable – Definition, Types and Examples

Table of Contents

Control Variable

Definition :

Control variable, also known as a “constant variable,” is a variable that is held constant or fixed during an experiment or study to prevent it from affecting the outcome. In other words, a control variable is a variable that is kept the same or held constant to isolate the effects of the independent variable on the dependent variable.

For example, if you were conducting an experiment to test how temperature affects plant growth, you might want to control variables such as the amount of water, the amount of sunlight, and the type of soil to ensure that these variables do not interfere with the results. By controlling these variables, you can isolate the effect of temperature on plant growth and draw more accurate conclusions from the experiment.

Types of Control Variables

Types of Control Variables are as follows:

Environmental Control Variables

These are variables related to the physical environment in which the experiment is conducted, such as temperature, humidity, light, and sound.

Participant Control Variables

These are variables related to the participants in the experiment, such as age, gender, prior knowledge, or experience.

Experimental Control Variables

These are variables that the researcher manipulates or controls to ensure that they do not affect the outcome of the experiment. For example, in a study on the effects of a new medication, the researcher might control the dosage, frequency, or duration of the treatment.

Procedural Control Variables

These are variables related to the procedures or methods used in the experiment, such as the order in which tasks are completed, the timing of measurements, or the instructions given to participants.

Equipment Control Variables

These are variables related to the equipment or instruments used in the experiment, such as calibration, maintenance, or proper functioning.

How to Control a Variable

To control a variable in a scientific experiment, you need to ensure that it is kept constant or unchanged throughout the experiment. Here are some steps to help you control a variable:

Identify the Variable

Start by identifying the variable that you want to control. This can be an environmental, subject, procedural, or instrumentation variable.

Determine the Level of Control Needed

Depending on the variable, you may need to exert varying levels of control. For example, environmental variables may require you to control the temperature, humidity, and lighting in your experiment, while subject variables may require you to select a specific group of participants that meet certain criteria.

Establish a Standard Level

Determine the standard level or value of the variable that you want to control. For example, if you are controlling the temperature, you may set the temperature to a specific degree and ensure that it is maintained at that level throughout the experiment.

Monitor the Variable

Throughout the experiment, monitor the variable to ensure that it remains constant. Use appropriate equipment or instruments to measure the variable and make adjustments as necessary to maintain the desired level.

Document the Process

Document the process of controlling the variable to ensure that the experiment is replicable. This includes documenting the standard level, monitoring procedures, and any adjustments made during the experiment.

Examples of Control Variables

Here are some examples of control variables in Scientific Experiments and Research:

• Environmental Control Variables Example: Suppose you are conducting an experiment to study the effect of light on plant growth. You would want to control environmental factors such as temperature, humidity, and soil nutrients. In this case, you might keep the temperature and humidity constant and use the same type and amount of soil for all the plants.
• Subject Control Variables Example : If you are conducting an experiment to study the effect of a new medication on blood pressure, you would want to control subject variables such as age, gender, and health status. In this case, you might select a group of participants with similar ages, genders, and health conditions to ensure that these variables do not affect the results.
• Procedural Control Variables Example : Suppose you are conducting an experiment to study the effect of distraction on reaction time. You would want to control procedural variables such as the time of day, the order of the tasks, and the instructions given to the participants. In this case, you might ensure that all participants perform the tasks in the same order, at the same time of day, and receive the same instructions.
• Instrumentation Control Variables Example : If you are conducting an experiment to study the effect of a new measurement device on the accuracy of readings, you would want to control instrumentation variables such as the type and calibration of the device. In this case, you might use the same type and model of the device and ensure that it is calibrated before each use.

Applications of Control Variable

Control variables are widely used in scientific research across various fields, including physics, biology, psychology, and engineering. Here are some applications of control variables:

• In medical research , control variables are used to ensure that any observed effects of a new treatment or medication are due to the treatment and not some other variable. By controlling subject variables such as age, gender, and health status, researchers can isolate the effects of the treatment and determine its effectiveness.
• In environmental research , control variables are used to study the effects of changes in the environment on various species or ecosystems. By controlling environmental variables such as temperature, humidity, and lighting, researchers can determine how different species adapt to changes in the environment.
• In psychology research, control variables are used to study the effects of different interventions or therapies on cognitive or behavioral outcomes. By controlling procedural variables such as the order of tasks, the length of time allotted for each task, and the instructions given to participants, researchers can isolate the effects of the intervention and determine its effectiveness.
• In engineering research, control variables are used to study the effects of different design parameters on the performance of a system or device. By controlling instrumentation variables such as the type of measurement device used and the calibration of instruments, researchers can ensure that the measurements are accurate and reliable.

Purpose of Control Variable

The purpose of a control variable in an experiment is to ensure that any observed changes or effects are a result of the manipulation of the independent variable and not some other variable. By keeping certain variables constant, researchers can isolate the effects of the independent variable and determine whether it has a significant effect on the dependent variable.

Control variables are important because they help to increase the reliability and validity of the experiment. Reliability refers to the consistency and reproducibility of the results, while validity refers to the accuracy and truthfulness of the results. By controlling variables, researchers can reduce the potential for extraneous or confounding variables that can affect the outcome of the experiment and increase the likelihood that the results accurately reflect the effect of the independent variable on the dependent variable.

Characteristics of Control Variable

Control variables have the following characteristics:

• Constant : Control variables are kept constant or unchanged throughout the experiment. This means that their values do not vary or change during the experiment. Keeping control variables constant helps to ensure that any observed effects or changes are due to the manipulation of the independent variable and not some other variable.
• Independent : Control variables are independent of the independent variable being studied. This means that they do not affect the relationship between the independent and dependent variables. By controlling for independent variables, researchers can isolate the effect of the independent variable and determine its impact on the dependent variable.
• Documented: Control variables are documented in the experiment. This means that their values and methods of control are recorded and reported in the results section of the research paper. By documenting control variables, researchers can demonstrate the rigor and transparency of their study and allow other researchers to replicate their methods.
• Relevant: Control variables are relevant to the research question. This means that they are chosen based on their potential to affect the outcome of the experiment. By selecting relevant control variables, researchers can reduce the potential for extraneous or confounding variables that can affect the outcome of the experiment and increase the reliability and validity of the results.
• Varied : Control variables can be varied across different conditions or groups. This means that different levels of control may be needed depending on the research question or hypothesis being tested. By varying control variables, researchers can test different hypotheses and determine the factors that affect the outcome of the experiment.

Advantages of Control Variable

The advantages of using control variables in an experiment are:

• Increased accuracy : Control variables help to increase the accuracy of the results by reducing the potential for extraneous or confounding variables that can affect the outcome of the experiment. By controlling for these variables, researchers can isolate the effect of the independent variable on the dependent variable and determine whether it has a significant impact.
• Increased reliability : Control variables help to increase the reliability of the results by reducing the variability in the experiment. By keeping certain variables constant, researchers can ensure that any observed changes or effects are due to the manipulation of the independent variable and not some other variable.
• Reproducibility: Control variables help to increase the reproducibility of the results by ensuring that the same results can be obtained when the experiment is repeated. By documenting and reporting control variables, researchers can demonstrate the rigor and transparency of their study and allow other researchers to replicate their methods.
• Generalizability : Control variables help to increase the generalizability of the results by reducing the potential for bias and increasing the external validity of the experiment. By controlling for relevant variables, researchers can ensure that their findings are applicable to a broader population or context.
• Causality : Control variables help to establish causality by ensuring that any observed changes or effects are due to the manipulation of the independent variable and not some other variable. By controlling for confounding variables, researchers can increase the internal validity of the experiment and establish a cause-and-effect relationship between the independent and dependent variables.

Disadvantages of Control Variable

There are some potential disadvantages or limitations of using control variables in an experiment:

• Complexity : Controlling for multiple variables can make an experiment more complex and time-consuming. This can increase the likelihood of errors and reduce the feasibility of the experiment, especially if the control variables require a lot of resources or are difficult to measure.
• Artificiality : Controlling for variables can make the experimental conditions artificial and not reflective of real-world situations. This can reduce the external validity of the experiment and limit the generalizability of the findings to real-world settings.
• Limited scope : Controlling for specific variables can limit the scope of the experiment and make it difficult to generalize the results to other situations or populations. This can reduce the external validity of the experiment and limit its practical applications.
• Assumptions: Controlling for variables requires making assumptions about which variables are relevant and how they should be controlled. These assumptions may not be valid or accurate, and the results of the experiment may be affected by uncontrolled variables that were not considered.
• Cost : Controlling for variables can be costly, especially if the control variables require additional resources or equipment. This can limit the feasibility of the experiment, especially for researchers with limited funding or resources.

Limitations of Control Variable

There are several limitations of using control variables in an experiment, including:

• Not all variables can be controlled : There may be some variables that cannot be controlled or manipulated in an experiment. For example, some variables may be too difficult or expensive to measure or control, or they may be affected by factors outside of the researcher’s control.
• Interaction effects : Control variables can interact with each other, which can lead to unexpected results. For example, controlling for one variable may have a different effect when another variable is also controlled, or when the two variables interact with each other. These interaction effects can be difficult to predict or control for.
• Over-reliance on statistical significance: Controlling for variables can increase the statistical significance of the results, but this may not always translate to practical significance or real-world significance. Researchers should interpret the results of an experiment in light of the practical significance, not just the statistical significance.
• Limited generalizability : Controlling for variables can limit the generalizability of the results to other populations or situations. If the control variables are not representative of other populations or situations, the results of the experiment may not be applicable to those contexts.
• May mask important effects : Controlling for variables can mask important effects that are related to the independent variable. By controlling for certain variables, researchers may miss important interactions between the independent variable and the controlled variable, which can limit the understanding of the causal relationship between the two.

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What Is a Variable in Science?

Understanding Variables in a Science Experiment

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Variables are an important part of science projects and experiments. What is a variable? Basically, a variable is any factor that can be controlled, changed, or measured in an experiment. Scientific experiments have several types of variables. The independent and dependent variables are the ones usually plotted on a chart or graph, but there are other types of variables you may encounter.

Types of Variables

• Independent Variable: The independent variable is the one condition that you change in an experiment. Example: In an experiment measuring the effect of temperature on solubility, the independent variable is temperature.
• Dependent Variable: The dependent variable is the variable that you measure or observe. The dependent variable gets its name because it is the factor that is dependent on the state of the independent variable . Example: In the experiment measuring the effect of temperature on solubility, solubility would be the dependent variable.
• Controlled Variable: A controlled variable or constant variable is a variable that does not change during an experiment. Example : In the experiment measuring the effect of temperature on solubility, controlled variable could include the source of water used in the experiment, the size and type of containers used to mix chemicals, and the amount of mixing time allowed for each solution.
• Extraneous Variables: Extraneous variables are "extra" variables that may influence the outcome of an experiment but aren't taken into account during measurement. Ideally, these variables won't impact the final conclusion drawn by the experiment, but they may introduce error into scientific results. If you are aware of any extraneous variables, you should enter them in your lab notebook . Examples of extraneous variables include accidents, factors you either can't control or can't measure, and factors you consider unimportant. Every experiment has extraneous variables. Example : You are conducting an experiment to see which paper airplane design flies longest. You may consider the color of the paper to be an extraneous variable. You note in your lab book that different colors of papers were used. Ideally, this variable does not affect your outcome.

Using Variables in Science Experiment

In a science experiment , only one variable is changed at a time (the independent variable) to test how this changes the dependent variable. The researcher may measure other factors that either remain constant or change during the course of the experiment but are not believed to affect its outcome. These are controlled variables. Any other factors that might be changed if someone else conducted the experiment but seemed unimportant should also be noted. Also, any accidents that occur should be recorded. These are extraneous variables.

Variables and Attributes

In science, when a variable is studied, its attribute is recorded. A variable is a characteristic, while an attribute is its state. For example, if eye color is the variable, its attribute might be green, brown, or blue. If height is the variable, its attribute might be 5 m, 2.5 cm, or 1.22 km.

• Earl R. Babbie. The Practice of Social Research , 12th edition. Wadsworth Publishing, 2009.
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• Six Steps of the Scientific Method
• Examples of Independent and Dependent Variables
• How To Design a Science Fair Experiment
• The Role of a Controlled Variable in an Experiment
• Scientific Variable
• What Are the Elements of a Good Hypothesis?
• Dependent Variable vs. Independent Variable: What Is the Difference?
• What Is the Difference Between a Control Variable and Control Group?
• Independent Variable Definition and Examples
• Null Hypothesis Examples
• What Is a Controlled Experiment?
• DRY MIX Experiment Variables Acronym
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Control Group Definition and Examples

The control group is the set of subjects that does not receive the treatment in a study. In other words, it is the group where the independent variable is held constant. This is important because the control group is a baseline for measuring the effects of a treatment in an experiment or study. A controlled experiment is one which includes one or more control groups.

• The experimental group experiences a treatment or change in the independent variable. In contrast, the independent variable is constant in the control group.
• A control group is important because it allows meaningful comparison. The researcher compares the experimental group to it to assess whether or not there is a relationship between the independent and dependent variable and the magnitude of the effect.
• There are different types of control groups. A controlled experiment has one more control group.

Control Group vs Experimental Group

The only difference between the control group and experimental group is that subjects in the experimental group receive the treatment being studied, while participants in the control group do not. Otherwise, all other variables between the two groups are the same.

Control Group vs Control Variable

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.

Types of Control Groups

There are different types of control groups:

• Placebo group : A placebo group receives a placebo , which is a fake treatment that resembles the treatment in every respect except for the active ingredient. Both the placebo and treatment may contain inactive ingredients that produce side effects. Without a placebo group, these effects might be attributed to the treatment.
• Positive control group : A positive control group has conditions that guarantee a positive test result. The positive control group demonstrates an experiment is capable of producing a positive result. Positive controls help researchers identify problems with an experiment.
• Negative control group : A negative control group consists of subjects that are not exposed to a treatment. For example, in an experiment looking at the effect of fertilizer on plant growth, the negative control group receives no fertilizer.
• Natural control group : A natural control group usually is a set of subjects who naturally differ from the experimental group. For example, if you compare the effects of a treatment on women who have had children, the natural control group includes women who have not had children. Non-smokers are a natural control group in comparison to smokers.
• Randomized control group : The subjects in a randomized control group are randomly selected from a larger pool of subjects. Often, subjects are randomly assigned to either the control or experimental group. Randomization reduces bias in an experiment. There are different methods of randomly assigning test subjects.

Control Group Examples

Here are some examples of different control groups in action:

Negative Control and Placebo Group

For example, consider a study of a new cancer drug. The experimental group receives the drug. The placebo group receives a placebo, which contains the same ingredients as the drug formulation, minus the active ingredient. The negative control group receives no treatment. The reason for including the negative group is because the placebo group experiences some level of placebo effect, which is a response to experiencing some form of false treatment.

Positive and Negative Controls

For example, consider an experiment looking at whether a new drug kills bacteria. The experimental group exposes bacterial cultures to the drug. If the group survives, the drug is ineffective. If the group dies, the drug is effective.

The positive control group has a culture of bacteria that carry a drug resistance gene. If the bacteria survive drug exposure (as intended), then it shows the growth medium and conditions allow bacterial growth. If the positive control group dies, it indicates a problem with the experimental conditions. A negative control group of bacteria lacking drug resistance should die. If the negative control group survives, something is wrong with the experimental conditions.

• Bailey, R. A. (2008).  Design of Comparative Experiments . Cambridge University Press. ISBN 978-0-521-68357-9.
• Chaplin, S. (2006). “The placebo response: an important part of treatment”.  Prescriber . 17 (5): 16–22. doi: 10.1002/psb.344
• Hinkelmann, Klaus; Kempthorne, Oscar (2008).  Design and Analysis of Experiments, Volume I: Introduction to Experimental Design  (2nd ed.). Wiley. ISBN 978-0-471-72756-9.
• Pithon, M.M. (2013). “Importance of the control group in scientific research.” Dental Press J Orthod . 18 (6):13-14. doi: 10.1590/s2176-94512013000600003
• Stigler, Stephen M. (1992). “A Historical View of Statistical Concepts in Psychology and Educational Research”. American Journal of Education . 101 (1): 60–70. doi: 10.1086/444032

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Difference Between Constant and Control

• Categorized under Physics , Science | Difference Between Constant and Control

As scientists continue to figure out how nature works, they do so by use of experiments, with an aim of searching for cause and effect relationships. These relationships are used to explain why things happen and allow one to predict what will happen if a certain event occurs. The role of these experiments hence is to observe and measure how changes occur in relation to other things. In an experiment, the things that change are referred to as variables.  

What is a Constant?

These are values that do not change during experiments.  

For example, in an experiment where one wants to test how the growth of plants is affected by the amount of water, factors like type of soil, temperature, type of plant and sunlight all stay the same in the course of the experiment. These are hence referred to as the constants in this experiment, while the amount of water is the control.    

Other examples include freezing and boiling points of water, the speed of light,  

What is Controls?

A controlled variable is a variable that could change but is intentionally kept constant in order to clearly show the relationship between dependent and independent variables. It is also a variable that is not of primary interest and hence constitutes a third factor whose influence is to be controlled or eliminated. These variables either need to be kept constant during the experiment or be monitored and recorded. This ensures that their influence can be assessed. Most experiments have more than one control.  

An example used to explain control variable is the effect of fertilizers on a plants growth, whereby the growth of plants differs based on the amount of fertilizer used. The fertilizer, in this case, is the control variable.  

Other examples include time, pressure and temperature.

Similarities between Constant Vs. Control

• Both are important in experiments as they determine the outcome

Differences between Constant and Control

A constant variable does not change. A control variable on the other hand changes, but is intentionally kept constant throughout the experiment so as to show the relationship between dependent and independent variables.  

Primary interest

While the constant is the variable of primary interest, the control is not; hence its influence can be controlled or eliminated.  

Constant vs. Control: Comparison Table

Summary of Constant vs. Control

In an experiment, both constant and control variables are important as they influence the outcomes of the experiments.  

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Cite APA 7 Njogu, T. (2019, June 24). Difference Between Constant and Control. Difference Between Similar Terms and Objects. http://www.differencebetween.net/science/difference-between-constant-and-control/. MLA 8 Njogu, Tabitha. "Difference Between Constant and Control." Difference Between Similar Terms and Objects, 24 June, 2019, http://www.differencebetween.net/science/difference-between-constant-and-control/.

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What are Constants in a Science Experiment? An Overview of their Role and Impact

By Happy Sharer

Introduction

A constant is a variable that remains unchanged throughout the duration of an experiment. In scientific experiments, constants play an important role in ensuring accuracy of results by keeping certain variables consistent. By controlling these variables and maintaining constant conditions throughout the course of the experiment, researchers are able to accurately measure the effect of the independent variable on the dependent variable.

Explaining the Basics of Constants in Science Experiments

To understand the role of constants in science experiments, it is important to first define what they are and provide examples of constants in different types of experiments.

What are constants?

Constants, or controlled variables, are factors that remain the same throughout the duration of an experiment. According to research conducted by Dr. Michael L. Wiederhold at Texas A&M University, “A constant is any factor that is held constant (unchanged) throughout an experiment” 1 . Constants can include anything from environmental factors such as temperature and humidity, to equipment used in the experiment, or even the type of material used.

Examples of constants in science experiments

The type of experiment being conducted will determine which constants must be kept consistent. For example, when conducting a chemical reaction, the temperature and pressure must remain the same throughout the experiment in order to ensure accurate results. Similarly, when conducting a physical experiment, such as measuring the speed of a falling object, the distance from the object to the ground must remain the same.

How to Identify and Control Constants in a Science Experiment

In order to ensure accurate results, it is important to identify and control all constants in an experiment. This can be done by carefully planning the experiment and taking into account all possible variables that could affect the results.

Identifying constants in an experiment

When designing an experiment, it is important to consider all the variables that could potentially affect the results. These variables can include environmental factors such as temperature and humidity, as well as the type of materials and equipment used in the experiment. Identifying all possible constants before beginning the experiment can help to ensure accuracy of results.

Controlling constants in an experiment

Once all the constants have been identified, they must then be controlled. This can be done by setting up the experiment in a way that keeps all the constants consistent. For example, if the experiment involves measuring the effects of temperature on a particular chemical reaction, the temperature should be kept constant throughout the experiment. Additionally, the same type of equipment should be used for each trial and the same materials should be used for each measurement.

Examining the Role of Constants in Ensuring Accurate Results

By controlling the constants in an experiment, researchers are able to accurately measure the effects of the independent variable on the dependent variable. This is essential in order to obtain reliable results from the experiment.

The importance of constants in scientific experiments

The importance of controlling constants in an experiment cannot be overstated. According to research conducted by Dr. Robert B. Goldberg at the University of California, Los Angeles, “The ability to control variables is a fundamental requirement for obtaining valid data in science” 2 . By controlling the constants in an experiment, researchers are able to measure the effects of the independent variable on the dependent variable more accurately. This helps to ensure that the results obtained from the experiment are reliable and valid.

How constants affect accuracy of results

If the constants in an experiment are not controlled, the results may be inaccurate. This is because the uncontrolled variables can interfere with the results of the experiment, making it difficult to accurately measure the effects of the independent variable. For example, if the temperature of an experiment is not kept constant, the results may be skewed due to the changes in temperature. Therefore, it is essential to control all constants in an experiment in order to ensure accuracy of results.

Tips for Maintaining Constant Conditions During a Science Experiment

Maintaining constant conditions during an experiment is essential for obtaining accurate results. There are several strategies and techniques that can be used to ensure that the constants in an experiment are kept consistent.

Strategies for maintaining constant conditions

One strategy for maintaining constant conditions is to use the same type of equipment for each trial. This ensures that all measurements are taken using the same device, which reduces the possibility of errors. Additionally, the same type of materials should be used for each measurement. For example, if measuring the effects of temperature on a chemical reaction, the same type of thermometer should be used for each trial.

Techniques for ensuring accuracy of results

Another technique for ensuring accuracy of results is to use multiple trials. This allows researchers to measure the effects of the independent variable on the dependent variable more accurately, as any errors or discrepancies can be averaged out across the multiple trials. Additionally, researchers should record all measurements and data collected during the experiment, so that any abnormalities or irregularities can be identified and addressed.

Analyzing the Impact of Constants on the Outcome of a Science Experiment

The impact of constants on the outcome of an experiment is significant. By controlling the constants, researchers are able to accurately measure the effects of the independent variable on the dependent variable, which is essential for obtaining reliable results.

The effect of constants on the outcome of an experiment

The effect of constants on the outcome of an experiment can be seen in the results. If the constants are not controlled, the results may be inaccurate or inconclusive. However, if the constants are controlled, the results will be more reliable and valid. This is because uncontrolled variables can interfere with the results of the experiment, making it difficult to accurately measure the effects of the independent variable.

The importance of constants in validating results

The importance of controlling constants in an experiment cannot be overstated. By controlling the constants, researchers are able to validate the results of the experiment. This is essential for ensuring that the results obtained from the experiment are reliable and valid. Additionally, controlling the constants helps to reduce the possibility of errors or inconsistencies that could skew the results.

Illustrating Examples of Constants in Different Types of Science Experiments

The type of experiment being conducted will determine which constants must be kept consistent. To further illustrate the role of constants in different types of experiments, several examples will be provided.

Examples of constants in different types of experiments

For example, when conducting a chemical reaction, the temperature and pressure must remain the same throughout the experiment in order to ensure accurate results. Similarly, when measuring the speed of a falling object, the distance from the object to the ground must remain the same. Additionally, when conducting a biological experiment, the environment in which the experiment is conducted must remain the same.

Examples of the impact of constants in different experiments

The impact of constants on the outcome of an experiment can be seen in the results. For example, if the temperature of a chemical reaction is not kept constant, the results may be skewed due to the changes in temperature. Similarly, if the distance from the object to the ground is not kept constant when measuring the speed of a falling object, the results may be inaccurate.

In conclusion, constants play an important role in scientific experiments. By controlling the constants, researchers are able to accurately measure the effects of the independent variable on the dependent variable, which is essential for obtaining reliable results. Additionally, controlling the constants helps to reduce the possibility of errors or inconsistencies that could skew the results. Finally, it is important to remember that the type of experiment being conducted will determine which constants must be kept consistent.

Overall, constants are an essential part of any scientific experiment and must be carefully considered and controlled in order to ensure accuracy of results. By following the tips and strategies outlined in this article, researchers can better understand the role of constants in science experiments and how to maintain constant conditions throughout the course of the experiment.

(Note: Is this article not meeting your expectations? Do you have knowledge or insights to share? Unlock new opportunities and expand your reach by joining our authors team. Click Registration to join us and share your expertise with our readers.)

Hi, I'm Happy Sharer and I love sharing interesting and useful knowledge with others. I have a passion for learning and enjoy explaining complex concepts in a simple way.

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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).

Table of contents

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.

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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

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.

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