The big idea: Every experiment is built on three kinds of variable. Getting them straight is the single most-tested data skill in the whole course — it appears on Paper 1B and runs right through your internal assessment (IA).
Independent variable (IV) — the ONE thing you deliberately change.
Dependent variable (DV) — the thing you measure (the outcome that responds).
Controlled variables (CVs) — everything else you keep the same so the test is fair.
A good experiment changes one IV, measures one DV, and controls all the rest.
- Independent variable (IV)
- The factor you deliberately change/manipulate. It is the cause you are testing. Plotted on the x-axis.
- Dependent variable (DV)
- The factor you measure as the result. It depends on (responds to) the IV. Plotted on the y-axis.
- Controlled variable (CV)
- A factor kept constant in every trial so it cannot affect the result. Also called a 'constant'.
- Fair test
- An experiment in which only the independent variable is changed, while all controlled variables are kept the same.
- Confounding variable
- A factor that was NOT kept constant and changes alongside the IV — it makes the result impossible to interpret.
| Type of variable | What it is | In our example (light → photosynthesis) |
|---|---|---|
| Independent (IV) | The ONE thing you deliberately CHANGE | Light intensity (the lamp distance: 10, 20, 30, 40, 50 cm) |
| Dependent (DV) | The thing you MEASURE — the outcome that responds | Rate of photosynthesis (bubbles of O₂ per minute) |
| Controlled (CVs) | Everything you keep CONSTANT so the test is fair | Temperature, CO₂ concentration, same pondweed, same time, same water volume |
Two memory hooks: 'I change the Independent.' The Independent variable is the one I (the experimenter) set, so it is Independent of the result.
'The Dependent Depends.' The Dependent variable depends on the IV — it is whatever you read off your measuring instrument.
Quick axis check: IV goes on the x-axis, DV goes on the y-axis ("DRY MIX" — Dependent–Responding–Y-axis; Manipulated–Independent–X-axis).
Naming the variables is only half the job. To trust your result you also need to set the IV precisely, control the rest, and include a control treatment to compare against.
Here is a complete, fair design worked out — including the numbers for how you'd set the independent variable and how you'd compare a treatment with its control.
Designing a fair test (the checklist)
- Pick ONE independent variable and decide its levels (at least 5 values give a good graph).
- Choose what to measure — the dependent variable — and how (which instrument, what units).
- List the controlled variables and say how you will keep each constant.
- Include a control treatment — a baseline with the factor absent / at zero — to compare against.
- Repeat each level (replicates) so you can take a mean and judge reliability.
What a control treatment is for: A control is the version of the experiment where the factor being tested is absent (or set to a baseline). It is not the same as a controlled variable.
Controlled variable = a factor kept constant (e.g. temperature).
Control treatment / control group = a whole baseline run with no treatment, so any difference you see in the treated groups must be due to the independent variable and nothing else.
| Treatment group(s) | Control group | |
|---|---|---|
| Independent variable | Set to the levels being tested (e.g. enzyme + inhibitor) | Set to a baseline / 'no treatment' (e.g. enzyme + plain water) |
| Purpose | Shows what happens WHEN the factor is applied | Shows the baseline — what happens WITHOUT it, for comparison |
| What it lets you conclude | — | Any difference from the control must be due to the independent variable, not something else |
Worked setup ① — fixing the IV by serial dilution: Say the IV is enzyme-substrate concentration and you want five levels: 100, 50, 25, 12.5 and 6.25 g dm⁻³. You make these by serial dilution — each tube is diluted by the same factor as the one before it.
The dilution factor here is , so each step halves the concentration:
, then , then , then .
To make each tube you mix equal volumes: take of the previous concentration of water → a total of at half the strength. Total volume () is a controlled variable — it stays the same in every tube.
Worked setup ② — comparing a treatment with its control (% change): Now suppose the control (no treatment) gives a rate of and the treated group gives . By how much did the treatment change the rate?
Use the percentage-change formula:
increase.
The control is what makes this number meaningful: without the baseline of , the value on its own tells you nothing about the treatment's effect.
| Question to ask | Which variable | Why it matters |
|---|---|---|
| What did I change on purpose? | Independent variable | It is the cause you are testing — there must be only ONE. |
| What did I measure to see the effect? | Dependent variable | It is the outcome / the data you collect and plot (usually on the y-axis). |
| What did I keep the same? | Controlled variables | If these change too, you can't tell which factor caused the result — the test is no longer fair. |
Why hold the controlled variables constant?: If a controlled variable is allowed to change at the same time as the IV, it becomes a confounding variable. You then can't tell whether the result was caused by your IV or by the thing that slipped — so the experiment is invalid.
Example: testing how temperature affects an enzyme, you must keep pH constant. If pH drifted as you warmed the tubes, a change in rate could be due to either temperature or pH — you couldn't say which.
Practice with real exam questions
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How this is tested: On Paper 1B (data-based) and in your IA, almost every investigation opens with variable questions:
• Identify the independent variable (the one deliberately changed) — 1 mark.
• Identify the dependent variable (the one measured) — 1 mark.
• State / name a controlled variable that must be kept constant — 1 mark — and sometimes explain why it must be controlled.
• Suggest the appropriate control treatment, or give a reason for including an untreated comparison group.
Answer with the specific quantity named in the scenario (e.g. 'water temperature in °C'), not a vague word like 'conditions'.
IB-style question — variables & control in a germination investigation
A student investigates how salt concentration affects the germination of radish seeds. They set up five dishes, each with 20 seeds on filter paper, watered daily with a salt solution of 0, 5, 10, 15 or 20 g dm⁻³, kept at 22 °C in the dark, and count how many seeds germinate after 7 days.
(a) Identify the independent variable. [1]
(b) Identify the dependent variable. [1]
(c) State two variables that must be controlled. [2]
(d) Explain the purpose of the 0 g dm⁻³ dish. [2]
How to score all six marks
- (a) Independent variable = the salt concentration of the watering solution (the thing deliberately changed: 0, 5, 10, 15, 20 g dm⁻³). [1]
- (b) Dependent variable = the number of seeds that germinate (out of 20) after 7 days — the outcome that is measured. [1]
- (c) Controlled variables (any TWO, each specific): temperature (22 °C), number of seeds per dish (20), volume of solution added daily, light (kept in the dark), seed type/age, time (7 days). [1 + 1]
- (d) The 0 g dm⁻³ dish is the CONTROL treatment. It has no salt, so it shows the baseline germination when the tested factor is absent. Any drop in germination in the salty dishes can then be attributed to the salt, not to something else. [Mark 1: it is the control / baseline with no salt. Mark 2: lets you attribute differences to salt / for comparison.]
Final answer
(a) Salt concentration of the solution. (b) Number of seeds germinating after 7 days. (c) Any two of: temperature, number of seeds, volume of solution, light, seed type, time. (d) It is the control — with no salt it gives the baseline germination, so any reduction in the salty dishes can be attributed to the salt.
✓ Why this scores full marks: Each variable is named with its specific quantity and unit (salt concentration in g dm⁻³, not just 'the solution'). The two controlled variables are distinct and concrete (temperature AND seed number — not 'the conditions' twice). And part (d) names the dish as the control and explains what it lets you conclude, which is where the second mark lives.
Common ways students drop these easy marks: Vague controlled variables — 'keep everything the same' or 'the environment' score nothing. Name a specific factor.
Swapping IV and DV — if you 'change' the thing you actually measured, you've reversed them. Ask: which one did I set, which one did I read off?
Confusing a controlled variable with the control treatment — a controlled variable is kept constant; the control treatment is a whole baseline run with no treatment.