Practical and data questions in A Level Biology reward a different set of skills from recall questions. You are not asked to remember a fact. You are asked to look at an experiment or a set of results and say something careful about it: which variable was changed, what was kept the same, what the figures show, which result looks wrong and what the evidence does and does not allow you to conclude. Students who know the content well can still lose marks here, because the answers are vague.
This guide focuses on those practical-method and data-handling skills for Cambridge International A Level Biology (9700). It is deliberately about reading, planning and evaluating experiments, not about revision tools. It belongs to our wider series on A Level exam technique by subject, which explains why the format of each paper changes how you should answer, and it pairs with our Mathematics guide to common A Level Mathematics exam mistakes, because many data questions in Biology also depend on careful calculation.
One note on the examples. Every data set and question below is an illustration written for this guide, not a question from a past paper, and the figures are invented to show a skill. Syllabuses and papers change, so check the current requirements on our A Level Biology page and in the official syllabus before relying on any detail.
How 9700 Assesses Practical and Data Skills
The points below come from Cambridge's 9700 syllabus document for examinations in 2025, 2026 and 2027, as checked on 27 October 2026. Always read the version that applies to your own exam year on Cambridge's 9700 syllabus page.
| Component | What the syllabus says |
|---|---|
| Paper 3: Advanced Practical Skills | A 2-hour paper worth 40 marks, testing practical skills such as manipulation, measurement and observation, presenting data, and analysis, conclusions and evaluation. Tasks may include investigations and work with a light microscope |
| Paper 5: Planning, Analysis and Evaluation | A 1 hour 15 minute paper worth 30 marks, with structured questions on planning, analysis and evaluation. The context may be outside the syllabus content, so the skill is applying method, not recalling a topic |
| Statistics | Paper 4 and Paper 5 may include descriptive statistics and the statistical tests named in the syllabus. Candidates are not expected to carry out every step of some calculations, but may be given partly completed ones to finish |
The second row matters most for revision. If the context of a Paper 5 question can be unfamiliar, no amount of memorising a single practical will cover it. What carries over is a way of thinking: identify the variables, decide how to measure, say how you would reduce error and describe how you would process the results.
Variables and Controls: Be Specific
The most common weakness in method answers is the phrase 'keep everything else the same'. It tells the examiner nothing. A good answer names the factor and says how it is kept constant. Take an illustrative investigation into the effect of temperature on the activity of catalase in liver, measured by the volume of oxygen collected from hydrogen peroxide in 60 seconds.
| Vague answer | Specific answer |
|---|---|
| Change the temperature. | The independent variable is temperature, set at 10, 20, 30, 40 and 50 °C using a thermostatically controlled water bath and checked with a thermometer |
| Measure the oxygen. | The dependent variable is the volume of oxygen collected in 60 seconds, measured with a gas syringe or an inverted measuring cylinder over water |
| Keep everything else the same. | Control the volume and concentration of hydrogen peroxide, the mass and surface area of liver pieces, the pH using a buffer solution and the time allowed for the mixture to reach the target temperature before starting |
Notice that each specific answer can be acted on by someone else. That is a useful test for any method: could another student repeat your experiment exactly from your description? If not, the answer is probably too vague to earn the mark.
Control experiments and controlled variables are different
Students often use these terms interchangeably. A controlled variable is a factor kept constant so that it cannot affect the result. A control experiment is a separate set-up used for comparison. In the catalase example, a control might use boiled and cooled liver, or water in place of the liver, to show that the oxygen really does come from enzyme activity. Say which one you mean.
Repeats and range
Two further points are easy to state weakly. Repeating each temperature at least three times lets you spot an anomaly and calculate a mean. The range should be wide enough to show the pattern and have enough values to show where it changes. Giving five temperatures from 10 to 50 °C is far more useful than two temperatures far apart.
Recording Results in a Table
A results table is marked for how clearly it is organised as well as for the numbers in it. In the same illustrative investigation, three repeats at each temperature might be recorded as follows.
| Temperature / °C | Volume of oxygen in 60 s / cm³ (repeat 1) | Repeat 2 | Repeat 3 | Mean / cm³ |
|---|---|---|---|---|
| 10 | 3.5 | 4.0 | 4.5 | 4.0 |
| 20 | 9.0 | 10.0 | 9.5 | 9.5 |
| 30 | 16.5 | 17.5 | 24.0 | 17.0 |
| 40 | 22.0 | 23.0 | 22.5 | 22.5 |
| 50 | 3.0 | 2.5 | 3.5 | 3.0 |
Several habits are visible here. The independent variable is in the first column, and every heading carries its unit, separated by a slash. The raw data are shown, not only the means. Every volume is given to one decimal place, matching the precision of the measuring instrument. And the mean for 30 °C is 17.0 cm³, not the mean of all three repeats, for a reason covered next.
Anomalies: Spot Them, Say Why and Handle Them Properly
At 30 °C the three repeats are 16.5, 17.5 and 24.0 cm³. The third value is far from the other two, while every other temperature has repeats within about 1 cm³ of each other. It is the anomalous result. An anomaly is a result that does not fit the pattern of the others, and noticing it is itself a skill.
| Weak response | Strong response |
|---|---|
| The mean for 30 °C is (16.5 + 17.5 + 24.0) ÷ 3 = 19.3 cm³. | The 24.0 cm³ result is anomalous, because the other repeats at every temperature agree to within about 1 cm³. Excluding it, the mean is (16.5 + 17.5) ÷ 2 = 17.0 cm³ |
A good answer also suggests a plausible cause, such as a larger piece of liver, an air bubble in the syringe or the mixture starting before it had reached 30 °C. Be careful with one point: do not simply delete an inconvenient value. Say that it is anomalous, say how you identified it and explain how you treated it. If you have no repeats, you cannot identify an anomaly with confidence, which is one more reason to plan repeats.
Drawing and Reading Graphs
Graph marks are usually awarded for choices that can be checked: the independent variable on the x-axis, the dependent variable on the y-axis, both axes labelled with units, a scale that uses most of the grid, points plotted accurately and a line drawn in the way the question asks. Follow the instruction given in the question or by your teacher about how to join points, since the appropriate line depends on the data and the context.
Two scale errors recur. One is an uneven scale, for example 0, 10, 20, 40 on the same axis. The other is a scale so small that the plotted points fill only a corner of the grid. When you read from a graph, give the unit with your value and, where the question asks for a figure from the graph, show on the graph how you found it.
Describing a Trend With Evidence
A 'describe' answer should state the overall pattern and then support it with figures from the data, including units. Using the means above, compare two answers to 'Describe the effect of temperature on the volume of oxygen produced'.
| Weak answer | Strong answer |
|---|---|
| As temperature increases, more oxygen is made, and then it goes down. | The volume of oxygen increases from 4.0 cm³ at 10 °C to 22.5 cm³ at 40 °C, then falls sharply to 3.0 cm³ at 50 °C. The greatest rise is between 20 and 30 °C, from 9.5 to 17.0 cm³ |
The strong answer uses the numbers to describe where the pattern changes. It can also include a simple calculation. The percentage increase from 20 to 30 °C is (17.0 − 9.5) ÷ 9.5 × 100, which is about 79%. State the working and the unit, or the percentage sign, and avoid quoting a figure to more decimal places than the data support.
Rate is not the same as total volume
If a question asks for the rate of reaction, divide the change by the time. For 30 °C, 17.0 cm³ in 60 seconds is 17.0 ÷ 60, about 0.28 cm³ s⁻¹. Check that the unit matches what you divided by. A volume given without a time is not a rate, and this is a frequent slip.
Writing a Conclusion the Evidence Supports
A conclusion should answer the question the experiment set out to answer, use the data to support it and stay within what the data show. Here, the results support the idea that the enzyme's activity increases with temperature up to a point and then falls sharply. An explanation using biology fits here: at higher temperatures the enzyme's tertiary structure can be disrupted, so the active site no longer fits the substrate, and the enzyme is denatured.
| Overreach | Evidence-based |
|---|---|
| The optimum temperature of catalase is 40 °C. | Of the temperatures tested, 40 °C gave the highest volume of oxygen, 22.5 cm³. The optimum could lie anywhere between 30 and 50 °C, because no values were tested between 40 and 50 °C |
This difference matters. Five temperatures cannot locate an optimum precisely. A careful answer says what was found and what remains unknown. Words such as 'supports' and 'is consistent with' are usually safer than 'proves'. Be equally careful with relationships in general: a correlation between two variables does not by itself show that one causes the other.
Evaluating the Method: Limitations and Improvements
Evaluation questions ask what limits the reliability or validity of the results and how to improve them. The strongest answers pair each limitation with a specific, matching improvement, and say why it helps. Avoid 'human error', which is too vague to be credited.
| Limitation | Specific improvement |
|---|---|
| Water bath temperature may have drifted during the 60 seconds | Use a thermostatically controlled water bath and record the temperature at the start and end of each run |
| Liver pieces were cut by eye, so surface area varied | Cut pieces with a cork borer and a ruler to the same dimensions, or use the same mass of liver for each run |
| Only one value was tested between 30 and 50 °C | Add 35, 45 and 55 °C so the point where activity falls is located more precisely |
| The mixture started reacting before reaching the target temperature | Place the hydrogen peroxide and the liver in the water bath separately for a few minutes before mixing them |
| Gas escaped before the syringe was connected | Connect the delivery tube before adding the liver, or use a bung in the flask |
Microscope Work: Drawings and Magnification
Practical papers can include light-microscope tasks. Two skills are reliably checked: making a clear drawing and calculating magnification. A drawing should use clear, continuous lines with no shading, show the outlines of the structures you can see and be large enough to show detail. Magnification is image size divided by actual size, and both must be in the same unit before dividing.
| Common mistake | Corrected method |
|---|---|
| A cell drawn 45 mm long has an actual length of 15 µm, so magnification = 45 ÷ 15 = ×3. | Convert to the same unit: 15 µm = 0.015 mm, so magnification = 45 ÷ 0.015 = ×3000 |
A check is simple. An answer of ×3 for a cell seen with a light microscope is implausibly small, which should prompt you to review the units. Showing the formula, the converted values and the answer is better than writing only the final number.
Using Statistics in Biology Questions
The syllabus names several statistical tests, and its notes say when each is appropriate. You do not need to memorise a general method for every case. You do need to know which test fits which kind of data, and to be able to interpret the result.
| Test | Used for (from the syllabus notes) |
|---|---|
| Chi-squared | Testing whether the difference between observed and expected frequencies of nominal data is significant, for example in breeding experiments. Only one row or column of data is expected |
| t-test | Testing the significance of the difference between two samples of continuous data, including samples with fewer than 30 values, where the data are normally distributed and the standard deviations are approximately the same |
| Pearson's linear correlation | Testing for a correlation between two sets of normally distributed continuous data, where a scatter diagram suggests a linear relationship |
| Spearman's rank correlation | Testing for a correlation between two sets of data that are not normally distributed, where the data are ordinal or can be ranked |
A short illustrative chi-squared example shows the pattern of reasoning. A cross between two heterozygous pea plants is expected to give tall and dwarf offspring in a 3 : 1 ratio. Among 160 offspring, 118 are tall and 42 are dwarf. The expected numbers are 120 and 40.
| Step | Working |
|---|---|
| State the null hypothesis | There is no significant difference between the observed and expected numbers |
| Calculate χ² | (118 − 120)² ÷ 120 + (42 − 40)² ÷ 40 = 0.033 + 0.100 = 0.133 |
| Degrees of freedom | Number of categories − 1 = 2 − 1 = 1 |
| Compare with the critical value | At p = 0.05 with 1 degree of freedom, the critical value is 3.84. Because 0.133 is less than 3.84, the difference is not significant |
| Conclude | The results are consistent with a 3 : 1 ratio, so the null hypothesis is not rejected. Any difference is likely due to chance |
Two details cause marks to slip. First, a result below the critical value does not prove the ratio is correct. It means the data give no evidence of a significant difference. Second, use the critical-value table supplied for your exam rather than relying on memory. For correlation tests, remember that values lie between −1 and +1, where 0 means no correlation.
Planning an Investigation
In a planning question, structure helps. A plan is easier to mark, and easier to follow, when it moves through the same stages each time. The list below is a general working structure, not a mark scheme, so adapt it to the wording of each question.
- 1
State the variables. Identify the independent and dependent variables, and say how each will be set and measured.
- 2
List the controls. Name the factors to keep constant and how, for example with a buffer, a water bath or a measured mass.
- 3
Describe the method. Give steps in order with quantities, apparatus and timings, clear enough for someone else to follow.
- 4
Plan the results. Say how many repeats, what range of values and what you will record, and how you will process the data.
- 5
Consider safety and reliability. Mention relevant hazards and precautions, and how you would reduce uncertainty.
Because Paper 5 contexts can be unfamiliar, practise with unfamiliar settings: a plant growth experiment, an enzyme from a different source or a sampling task in a habitat. Ask the same questions each time and the structure will transfer.
Common Mistakes at a Glance
| Mistake | What to do instead |
|---|---|
| Writing 'keep everything else the same' | Name each controlled variable and how you will keep it constant |
| Table headings with no units | Write the quantity and unit, such as Volume / cm³, and use the same decimal places throughout |
| Including an anomaly in the mean without comment | Identify it, justify why, and say how you treated it |
| Describing a trend without any figures | Quote values with units and say where the pattern changes |
| Claiming an exact optimum from a few values | State the tested value that gave the best result and what was not tested |
| Mixing units in a magnification calculation | Convert to the same unit before dividing |
| Using 'human error' as a limitation | Give a specific source of error with a specific improvement |
How to Practise These Skills
These skills improve with deliberate practice on real material, not with reading about them. Work through the practical and data questions in genuine papers from our A Level past papers pages, write your answers in full and then mark them strictly against the official mark scheme. Keep a log of what you lost marks on, grouped into categories such as variables, units, anomalies, conclusions and evaluation.
A habit worth building is to take any table from a past paper and, before reading the questions, describe the pattern in a sentence with two figures from it. Then ask what you would change to make the data more reliable. That small exercise rehearses the thinking that data questions demand. For the wider picture of how subjects differ, return to our guide to A Level exam technique by subject, and for the calculation side of data work, see our guide to common A Level Mathematics exam mistakes. For the calculation side of Physics, see our guide to A Level Physics calculation questions. You can also use our A Level practice pages for extra questions on the content itself.
Practise the subjects you choose
Work through topic questions and past papers for your A Level subjects, and get your answers marked with the reasoning behind each mark.
