AQA GCSE Biology Paper 2 required practicals: exactly what the exam asks

The exam rarely asks you to describe the entire practical from scratch. It gives you the context and then asks one of five question types. Know these and you can answer any required practical question without memorising a script.

• Identify the variables: "name the independent variable", "state one control variable", "what was measured" — these are one-mark recall questions.
• Explain a method step: "why were random coordinates used?", "why was the experiment repeated?" — these need a reason, not just a description.
• Suggest an improvement: "suggest how the student could make their results more reliable" — this needs a specific change and a specific reason.
• Draw a conclusion from data: "what does the data suggest about the relationship between X and Y?" — use the numbers, quote the trend, add the biology.
• Evaluate the method: "give one limitation of this method" — name what is wrong and why it affects the result.

This practical investigates how a factor — caffeine, practice, distraction, or time of day — affects reaction time. The method uses a ruler drop: one person drops it, the other catches it, and the distance fallen is converted to reaction time using a table or formula.

• Independent variable: the factor being changed (e.g. whether the participant has consumed caffeine).
• Dependent variable: the distance the ruler falls before it is caught, converted to reaction time in milliseconds.
• Control variables: same ruler, same starting position (ruler held at 0 cm level with hand), same person dropping, same time of day, same dominant hand used to catch.
• Why repeat? To identify anomalous results and calculate a mean, making the result more reliable.
• Why use the same person dropping without a countdown? A predictable countdown introduces anticipation, which would test anticipation rather than reaction time, making the test invalid.
• Improvement: use a computer-based reaction time test to remove human error in dropping the ruler and to measure time directly, improving accuracy.

This practical investigates how seedlings respond to light from one direction. Seedlings are grown in a box with a hole on one side so light enters from a single direction, and the angle of bending is measured after a set time.

• Independent variable: whether light is present from one side (or the direction of the light source, or the wavelength of light, depending on the specific investigation).
• Dependent variable: the angle of bending of the seedling shoot, measured in degrees.
• Control variables: same species of seedling, same age and size, same temperature, same volume of water, same duration of exposure, same intensity of light.
• Why use multiple seedlings per condition? Individual seedlings vary in their response. Using several and calculating a mean makes the result more reliable.
• Why cover some seedlings completely (control group)? To show that bending towards light is caused by the light, not by a growth pattern that would happen anyway — this makes the test valid.

This practical investigates the distribution or abundance of organisms in a habitat. Quadrats measure abundance in a defined area. Transects measure how distribution changes across an environmental gradient.

• Independent variable for quadrats: the habitat or condition being compared (e.g. sunny vs shaded area).
• Dependent variable: the number of organisms counted, or the percentage cover of a species, within each quadrat.
• Control variables: same size quadrat, same method of counting (count every plant rooted inside the quadrat, or use the same percentage cover scale consistently).
• Why use random coordinates? Random placement prevents the investigator from unconsciously choosing areas with more or fewer organisms, which would bias the results.
• Why use many quadrats? More quadrats give a more representative sample and a more reliable mean.
• How to estimate population size: mean number per quadrat × total area of habitat ÷ area of one quadrat.
• Transect method: lay a tape measure across the habitat gradient, place quadrats at regular intervals (e.g. every 2 m), record species at each point, and measure the relevant abiotic factor (e.g. light intensity, soil moisture).

This practical investigates how a variable — usually temperature, pH, or oxygen availability — affects the rate of decay. A common setup uses milk with lipase and a pH indicator: as the lipase breaks down the fat in milk, pH drops and the indicator changes colour. Time to colour change is used as a measure of enzyme activity, which reflects the rate of decomposition.

• Independent variable: temperature (or pH, or oxygen availability, depending on the investigation).
• Dependent variable: time taken for the indicator to change colour, or rate of colour change.
• Control variables: same volume of milk, same concentration and volume of lipase, same starting pH, same indicator volume, same type of milk.
• Why use a water bath? To keep temperature constant throughout the reaction. Temperature affects enzyme activity, so any fluctuation would make it unclear whether the results reflect the intended temperature or a change in conditions.
• Safety: clean up any spillages of biological material, wash hands after handling microorganisms or biological samples, and dispose of cultures safely.
• Why is this a model for decay? Decomposers digest organic molecules using enzymes. Factors that affect enzyme activity (temperature, pH) also affect the rate of real decay in the environment.

AQA uses the same set of terms across every practical question. Learn what each one means precisely, not approximately, and apply them without hesitation.

• Valid: the method tests what it is supposed to test. A valid experiment has appropriate controls and measures the right thing.
• Reliable: the results are consistent when the experiment is repeated. Improved by taking multiple measurements and calculating a mean.
• Accurate: measurements are close to the true value. Improved by using more precise instruments or calibrating equipment.
• Precise: repeated measurements are close to each other. Improved by careful technique and consistent method.
• Anomalous: a result that does not fit the pattern. Should be identified and excluded from the mean, then the measurement repeated if possible.
• Mean: the sum of all values divided by the number of values. Gives a better estimate than any single result because it smooths out random variation.