What Is a Variable?

A variable is something that can change or be measured during a scientific investigation.

Imagine investigating whether the amount of sunlight affects the growth of a plant.

Several things could change:

• Amount of sunlight
• Plant height
• Amount of water
• Type of plant
• Type of soil
• Temperature

These are all variables.

However, they do not all have the same role in the experiment.

Scientists usually identify three main types:

• Independent variable
• Dependent variable
• Controlled variables

Understanding these variables is essential for designing a fair test.

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The Independent Variable

The independent variable is the variable that the scientist deliberately changes.

A good question to ask is:

What am I changing?​

Suppose we investigate:

How does the amount of sunlight affect plant growth?

The scientist deliberately changes the amount of sunlight.

Therefore:

Independent variable = amount of sunlight​

Only one independent variable should normally be deliberately changed in a fair test.

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The Dependent Variable

The dependent variable is the variable that is measured or observed.

It changes in response to the independent variable.

Ask:

What am I measuring?​

In our plant experiment, we measure the growth of the plants.

Therefore:

Dependent variable = plant growth​

We might measure plant growth as:

height in centimetres

This makes the dependent variable quantitative and easier to compare.

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Controlled Variables

Controlled variables are variables that are kept the same during an experiment.

Ask:

What must I keep the same?​

In the plant experiment, controlled variables could include:

• Type of plant
• Starting size of the plants
• Amount of water
• Type of soil
• Size of the pot
• Temperature
• Length of the experiment

These variables should remain as constant as possible.

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The Three Types of Variables

A useful way to remember the variables is:

Another useful pattern is:

Change → Measure → Control​

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An Example Investigation

Suppose a student wants to investigate:

How does water temperature affect the time taken for sugar to dissolve?

First identify the variables.

Independent Variable

What does the student deliberately change?

Water temperature​

Dependent Variable

What does the student measure?

Time taken for the sugar to dissolve​

Controlled Variables

What should remain the same?

Possible controlled variables include:

• Mass of sugar
• Volume of water
• Type of sugar
• Size of sugar particles
• Container
• Stirring method

If these variables are controlled, the student can investigate the effect of temperature alone.

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Why Should We Change Only One Independent Variable?

Suppose a student wants to investigate how temperature affects dissolving.

In one experiment, the student uses:

• 20^oC water
• 5 g sugar

In another experiment, the student uses:

• 60^oC water
• 20 g sugar

There is a problem.

Two variables have changed:

• Temperature
• Mass of sugar

If the dissolving time changes, we cannot tell which variable caused the difference.

A better investigation would use:

Now only the temperature changes.

This allows us to investigate its effect more clearly.

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What Is a Fair Test?

A fair test is an investigation in which only the independent variable is deliberately changed while other relevant variables are controlled.

The basic idea is:

Change one thing and measure what happens.​

This helps scientists determine whether the independent variable is responsible for changes in the dependent variable.

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Why Are Controlled Variables Important?

Controlled variables make an experiment more valid.

Imagine investigating whether different amounts of fertiliser affect plant growth.

Plant A receives fertiliser and also receives much more water.

Plant B receives no fertiliser and receives less water.

If Plant A grows more, what caused the difference?

Was it:

• The fertiliser?
• The extra water?
• Both?

We cannot tell.

Water has become a confounding variable.

If the amount of water is kept constant, we can more confidently investigate the effect of fertiliser.

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Control Does Not Always Mean Zero

A controlled variable is not necessarily something that is removed from an experiment.

It is something that is kept consistent.

For example, if the amount of water is controlled at:

50 mL

every plant should receive approximately:

50 mL

The water is still present. Its amount simply does not change between experimental conditions.

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Identifying Variables from a Question

Scientific investigation questions often have the form:

How does X affect Y?

Usually:

X = independent variable

and:

Y = dependent variable

For example:

How does temperature affect reaction rate?

Therefore:

IV = temperature​

 

DV = reaction rate​

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More Examples

How does light intensity affect the rate of photosynthesis?

Independent variable:

Light intensity​

Dependent variable:

Rate of photosynthesis​

Possible controlled variables:

• Type of plant
• Temperature
• Carbon dioxide concentration
• Amount of water

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How does the length of a pendulum affect its period?

Independent variable:

Length of pendulum​

Dependent variable:

Period​

Possible controlled variables:

• Mass of pendulum bob
• Release angle
• Method of timing

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How does concentration affect reaction time?

Independent variable:

Concentration​

Dependent variable:

Reaction time​

Possible controlled variables:

• Temperature
• Volume of reactants
• Type of reactants
• Equipment used

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Variables and Graphs

Variables also determine how experimental graphs are constructed.

The independent variable is normally plotted on the:

x-axis​

The dependent variable is normally plotted on the:

y-axis​

For example, if investigating how temperature affects reaction rate:

x-axis = Temperature

 

y-axis = Reaction rate

This allows us to see how the dependent variable changes in response to the independent variable.

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What Is a Hypothesis?

A hypothesis is a testable prediction about the relationship between variables.

A good hypothesis should clearly identify:

• The independent variable
• The dependent variable
• The expected relationship

A common structure is:

If [independent variable changes], then [dependent variable will change], because [scientific reason].

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Writing a Clear Hypothesis

Suppose the investigation asks:

How does water temperature affect the rate at which sugar dissolves?

A weak hypothesis might be:

The sugar will dissolve.

This does not describe the expected relationship between the variables.

A better hypothesis is:

If the temperature of the water increases, then the sugar will dissolve more quickly.

An even stronger hypothesis includes scientific reasoning:

If the temperature of the water increases, then the sugar will dissolve more quickly because the water particles have greater kinetic energy and move more rapidly.

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Another Hypothesis Example

Investigation:

How does the amount of light affect plant growth?

Independent variable:

Amount of light

Dependent variable:

Plant growth

Possible hypothesis:

If the amount of light a plant receives increases, then its growth will increase because light provides the energy required for photosynthesis.

The prediction directly connects the two variables.

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Hypotheses Must Be Testable

Consider:

Plants like sunlight.

This is not a strong scientific hypothesis because "like" is difficult to measure scientifically.

A better statement would be:

If the number of hours of light per day increases, then the average increase in plant height over 14 days will increase.

Now both variables can be measured:

Light exposure in hours

and:

Plant growth in centimetres

This makes the hypothesis testable.

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Operational Variables

Scientists often need to describe exactly how a variable will be changed or measured.

Instead of saying:

Plant growth

we could specify:

Change in plant height in centimetres after 14 days.

Instead of saying:

Amount of light

we could specify:

Number of hours of light received each day.

Clear definitions make experiments easier to repeat and results easier to compare.

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Designing an Investigation

Suppose you want to investigate:

How does the concentration of salt in water affect the time required for an ice cube to melt?

A good experimental design might be:

Independent Variable

Concentration of salt solution.

For example:

0%, 5%, 10%, 15%

 

Dependent Variable

Time required for the ice cube to melt.

Measured in:

seconds

Controlled Variables

Keep constant:

• Size of ice cube
• Starting temperature
• Volume of solution
• Container
• Room conditions

The experiment changes one main variable while controlling the others.

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Repeated Measurements

Even when variables are carefully controlled, measurements may vary.

Scientists therefore often repeat measurements.

For example, instead of measuring each condition once:

32 s

we might repeat it three times:

32 s, 30 s, 31 s

and calculate a mean:

Repeating measurements can improve the reliability of experimental results.

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Variables, Validity, and Reliability

These ideas are closely connected but have different meanings.

Variables

Identify what is changed, measured, and controlled.

Validity

A valid experiment actually tests the intended relationship.

Controlling relevant variables helps improve validity.

Reliability

Reliable results are reasonably consistent when measurements are repeated.

Repeating measurements helps scientists assess and improve reliability.

Good experimental design requires attention to all three.

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Common Mistakes When Identifying Variables

Mistake 1: Confusing Independent and Dependent Variables

Remember:

Independent = change​ Dependent = measure​

 

Mistake 2: Calling Everything a Controlled Variable

Only variables that could affect the results and should remain constant need to be controlled.

Mistake 3: Changing Several Variables at Once

This makes it difficult to determine which variable caused the observed effect.

Mistake 4: Using Variables That Cannot Be Measured Clearly

Instead of "plant health," use something measurable such as:

plant height (cm)

or:

number of leaves

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A Variables Checklist

Before beginning an investigation, ask:

Independent variable

• What will I deliberately change?
• What values or conditions will I test?

Dependent variable

• What will I measure?
• What units will I use?
• How will I measure it?

Controlled variables

• What other factors could affect my results?
• How will I keep them constant?

Hypothesis

• What relationship do I predict?
• Does my hypothesis clearly connect the independent and dependent variables?
• Can my prediction actually be tested?

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Did You Know?

Scientists cannot always control every variable.

In laboratory experiments, scientists can often control temperature, quantities, equipment, and other conditions carefully.

However, scientists studying ecosystems, weather, populations, or space may have much less control over the conditions.

They must instead collect large amounts of data and use statistical methods to determine whether observed relationships are meaningful.

This is one reason experimental design is such an important part of science.

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Key Vocabulary

Variable – A factor that can change or be measured during an investigation.

Independent variable – The variable deliberately changed by the investigator.

Dependent variable – The variable measured or observed in response to the independent variable.

Controlled variable – A relevant variable kept constant during an investigation.

Fair test – An investigation in which one independent variable is deliberately changed while relevant variables are controlled.

Hypothesis – A testable prediction about the relationship between variables.

Validity – The extent to which an investigation tests what it is intended to test.

Reliability – The consistency of results when measurements are repeated.

Confounding variable – An uncontrolled factor that may affect the dependent variable and make the results difficult to interpret.

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Key Takeaways

• A variable is something that can change or be measured in an investigation.
• The independent variable is deliberately changed.
• The dependent variable is measured or observed.
• Controlled variables are kept constant so they do not unfairly influence the results.
• A useful memory aid is: change, measure, control.
• Only one independent variable should normally be deliberately changed in a fair test.
• Controlling relevant variables helps make an experiment more valid.
• The independent variable is normally plotted on the x-axis and the dependent variable on the y-axis.
• A good hypothesis predicts how the dependent variable will respond to changes in the independent variable.
• Strong hypotheses are specific, measurable, and testable.
• Clearly defining how variables will be changed and measured improves experimental design.
• Repeated measurements help scientists determine whether results are reliable.