On this page
- The three types of variable
- Example 1: temperature and reaction rate
- Example 2: concentration and electrical conductivity
- Example 3: a chromatography investigation
- Control variables vs a control experiment
- Why control variables matter: the fair test
- Writing a testable hypothesis
- Continuous and categoric variables
- Identifying variables in exam questions
- Practice: identify the variables
- Common mistakes
- Key takeaways
Every good experiment asks a simple question: if I change this, what happens to that? To get a trustworthy answer, you have to change only one thing at a time and keep everything else the same. That’s the whole idea behind variables, and understanding them is the key to designing experiments, writing hypotheses and answering practical questions in exams.
The three types of variable
| Variable | What it is | In a graph | Question to ask |
|---|---|---|---|
| Independent | the one thing you deliberately change | x-axis | “What am I changing?” |
| Dependent | the thing you measure to see the effect | y-axis | “What am I measuring?” |
| Control | everything else that could affect the result, kept the same | not plotted | “What must I keep the same?” |
A memory aid: the dependent variable depends on the independent variable.
Example 1: temperature and reaction rate
Investigating how temperature affects the rate of reaction between magnesium ribbon and hydrochloric acid.
- Independent: temperature of the acid (e.g. 20, 30, 40, 50 °C)
- Dependent: rate of reaction, measured as the volume of hydrogen collected in the first 30 seconds (or time for the magnesium to disappear)
- Control variables:
- concentration of acid
- volume of acid
- length (mass) of magnesium ribbon
- surface area of the magnesium (same type of ribbon, not powder)
- how the mixture is stirred (or not stirred)
If the concentration of acid changed between runs as well as the temperature, you couldn’t tell which change caused any difference in rate.
Example 2: concentration and electrical conductivity
Investigating how the concentration of sodium chloride solution affects its electrical conductivity.
- Independent: concentration of NaCl
- Dependent: conductivity (or current at fixed voltage)
- Control variables: temperature, distance between electrodes, electrode area, depth of immersion, voltage
Example 3: a chromatography investigation
Investigating how the polarity of the solvent affects the Rf value of a dye.
- Independent: proportion of ethanol in the ethanol–water solvent
- Dependent: Rf value of the dye
- Control variables: type of paper, temperature, solvent depth, spot size, tank covered with a lid
Control variables vs a control experiment
These sound similar but aren’t the same:
- Control variables are factors kept constant throughout.
- A control experiment (or “control”) is a separate run used for comparison, where the independent variable is absent or set to a baseline.
For example, when testing whether a catalyst speeds up the decomposition of hydrogen peroxide, a control experiment would be hydrogen peroxide with no catalyst, under otherwise identical conditions. Any difference can then be attributed to the catalyst.
Why control variables matter: the fair test
A fair test is one where only the independent variable changes. If other factors also change, the results are confounded: you can’t tell which factor caused the effect. See what makes a fair test.
How to control variables in practice:
| Variable | How to control it |
|---|---|
| Temperature | water bath; measure at start and end |
| Volume | same measuring equipment, ideally pipette or burette |
| Concentration | use the same stock solution |
| Mass or surface area of a solid | same mass, same particle size, same batch |
| Stirring | same stirring method, or a magnetic stirrer at fixed speed |
| Time | same timing method and end point |
| Light | same position in the lab |
Some variables are hard to control completely, such as room temperature or humidity. In that case, you monitor them and discuss their effect in your evaluation.
Writing a testable hypothesis
A hypothesis is a prediction about how the independent variable will affect the dependent variable, ideally with a scientific reason. A useful pattern:
If [independent variable changes in this way], then [dependent variable will change in this way], because [scientific explanation].
Example: If the concentration of hydrochloric acid is increased, then the rate of reaction with marble chips will increase, because there will be more acid particles in the same volume, so collisions with the marble surface will be more frequent.
A good hypothesis is:
- testable: you could do an experiment to check it
- specific: it names both variables and the expected direction of change
- justified: it gives a scientific reason
Continuous and categoric variables
- Continuous variables can take any value in a range: temperature, concentration, volume, time. Plot them on a line graph or scatter graph.
- Categoric variables have distinct categories: type of metal, type of catalyst, brand of antacid. Present them in a bar chart.
The type of independent variable decides how you should present your results. See drawing good graphs.
Identifying variables in exam questions
Exam questions often describe an experiment and ask you to identify variables. A reliable approach:
- Find the phrase “investigate how X affects Y”. X is independent; Y is dependent.
- Look for how Y is measured; that’s the actual dependent variable (for example, “volume of gas in 60 s”, not just “rate”).
- List everything else that could affect Y; those are control variables.
Example question: A student investigates how the mass of calcium carbonate affects the volume of carbon dioxide produced with excess hydrochloric acid.
- Independent: mass of calcium carbonate
- Dependent: volume of CO₂ (measured with a gas syringe)
- Control: concentration and volume of acid, temperature, particle size of the calcium carbonate
Practice: identify the variables
A student investigates how the surface area of zinc affects the rate of reaction with sulfuric acid, using zinc powder, small granules and large granules, and measuring the volume of hydrogen produced in one minute.
- Independent: surface area of zinc (a categoric variable here: powder, small granules, large granules), so the results suit a bar chart.
- Dependent: volume of hydrogen collected in one minute.
- Control: mass of zinc, concentration and volume of acid, temperature, and the method of collecting gas.
Common mistakes
- Mixing up independent and dependent. The one you choose values for is independent.
- Listing the independent variable as a control variable. It can’t be both.
- Vague dependent variables. “Rate” is vague; “time for the cross to disappear” or “volume of gas after 30 s” is measurable.
- Giving a control variable without saying how it’s controlled. “Temperature” is better written as “temperature, kept at 25 °C with a water bath”.
- Forgetting that a catalyst, light or stirring can also be variables.
- Choosing too few values. With only two or three values of the independent variable, it’s hard to see whether a relationship is a straight line or a curve; aim for at least five.
Key takeaways
- Independent variable: what you change (x-axis). Dependent: what you measure (y-axis). Control: what you keep the same.
- Controlling variables makes a fair test, so changes in the dependent variable can be linked to the independent variable.
- A control experiment is a separate baseline run for comparison.
- A good hypothesis follows “if… then… because…”.
- Continuous variables go on line graphs; categoric variables on bar charts.
Advertisement