OCR A Level Biology A: Planning, implementing, analysing and evaluating practicals (H420)

This guide covers section 1.1 of the OCR A Level Biology A specification (H420, Version 4.1, April 2026, first assessment in 2025): practical skills assessed in a written examination. That is learning outcomes 1.1.1(a)-(c) planning, 1.1.2(a)-(c) implementing, 1.1.3(a)-(d) analysis and 1.1.4(a)-(e) evaluation. The specification says these skills are embedded throughout the content. Use this page with the revision notes and the practice set. See the OCR A Level Biology hub, the printable checklist and the free diagnostics.

What this unit covers

Outcome	What you must be able to do
1.1.1(a)-(c)	Design experiments, control variables, judge whether a method is appropriate
1.1.2(a)-(c)	Use apparatus correctly, use units, present data
1.1.3(a)-(d)	Process results, use significant figures, plot graphs, find gradients and intercepts
1.1.4(a)-(e)	Evaluate, find anomalies and limitations, judge precision, accuracy and uncertainty, suggest improvements

1.1.1 Planning

(a) Experimental design

A plan says what you will change, what you will measure and how you will keep everything else the same. You must also apply biology from elsewhere in the specification to the setting you are given.

Worked plan. Investigate how pH affects the rate at which catalase in a plant extract breaks down hydrogen peroxide.

• Independent variable: pH, using buffer solutions at 4, 5, 6, 7, 8 and 9.
• Dependent variable: volume of oxygen released in a fixed time, collected in a gas syringe and read in cm³.
• Method: mix the same volumes of extract, buffer and hydrogen peroxide in a flask, connect the syringe at once and start the stopwatch. Read the volume at 60 s. Repeat three times at each pH.
• Biology applied: pH changes the ionic bonds that hold the tertiary structure of the enzyme, so the shape of the active site changes.
• Apparatus choice: a gas syringe measures a gas volume directly and more precisely than a measuring cylinder inverted over water.

(b) Variables to control

Control any variable that would change the dependent variable if it drifted. For the plan above:

Variable	How to control it
Temperature	Water bath at 25 °C, checked with a thermometer
Enzyme concentration	Same extract, same volume, one batch
Substrate concentration and volume	One stock of hydrogen peroxide, same measuring syringe
Buffer volume	Same volume every time

Say how you control each one. Differences between individual organisms cannot be controlled, so use repeats and larger samples.

A control experiment differs from a control variable. Here it would be extract boiled for several minutes, which should release no oxygen. It shows the oxygen comes from the enzyme.

(c) Is the method appropriate?

Check each part of a given method against the aim. Does it measure the dependent variable directly and at a suitable scale? Does the independent variable cover a wide enough range with enough values? Are there repeats? Does it give a quantity you can calculate with?

1.1.2 Implementing

(a) Using apparatus and techniques correctly

Be ready to describe correct use briefly: read a meniscus at eye level; zero the balance with the container on it; calibrate an eyepiece graticule against a stage micrometer; set a colorimeter to zero with a blank; make a dilution series from measured volumes.

(b) Units

Give every measurement a unit and convert before you calculate.

• 1 cm³ = 1000 mm³, and 1 mm = 1000 µm.
• A rate has a compound unit: cm³ s⁻¹ for volume per second, mm min⁻¹ for distance per minute.

Worked example. A micrograph of a cell is 45 mm long at a magnification of ×900. Actual length = image length ÷ magnification = 45 ÷ 900 = 0.05 mm. Multiply by 1000: 50 µm.

(c) Presenting observations and data

• Draw a results table before you start, with the independent variable in the first column.
• Put quantity and unit in the column headings, for example “Volume of oxygen / cm³”.
• Record raw data to the resolution of the instrument: a syringe marked in 1 cm³ steps gives whole numbers or halves, not 7.35.
• Describe qualitative results accurately (brick-red precipitate, blue-black colour), not “it changed”.
• Choose the chart to fit the data (see 1.1.3(d)).

1.1.3 Analysis

(a) and (b) Processing and interpreting results

Mathematical skills in practical contexts include means, ranges, percentage change, ratios, magnification and rates. State the average you use and say what you left out and why.

Worked example, percentage change. A carrot cylinder has mass 1.84 g before soaking and 2.03 g after.

change = 2.03 - 1.84 = 0.19 g
percentage change = 0.19 / 1.84 x 100 = 10.3 %  (a gain)

A valid conclusion matches the aim, quotes values from the data, claims only what the range tested supports and is explained with biology. “The rate rose from 0.8 cm³ s⁻¹ at 20 °C to 1.6 cm³ s⁻¹ at 30 °C because more molecules had enough energy to form enzyme-substrate complexes” beats “temperature increases rate”. A correlation does not show cause unless other variables were controlled.

(c) Significant figures

Give a calculated answer to the same number of significant figures as the least precise value used.

Oxygen collected: 16.4 cm3 (3 s.f.)    Time: 35 s (2 s.f.)
Rate = 16.4 / 35 = 0.4685...
Answer: 0.47 cm3 s^-1  (2 s.f.)

Round only at the end.

(d) Graphs, gradients and intercepts

(i) Axes and scales. Independent variable on the x-axis, dependent variable on the y-axis. Label each with the quantity and unit, such as “Time / min”. Use a scale that fills more than half of the grid, in equal steps. Use a line graph for continuous data, a bar chart for categories, a histogram for grouped continuous data and a scattergram to look for a relationship. Plot points accurately and draw a smooth curve or straight line of best fit; do not join the dots.

(ii) Gradient. For a straight line, choose two points on the line, far apart. Gradient = (y₂ − y₁) ÷ (x₂ − x₁). The unit is the y unit divided by the x unit.

Worked example. A potometer records bubble movement in a capillary tube of diameter 1.0 mm.

Time / min	0	5	10	15	20	25
Distance moved / mm	0	13	29	41	57	69

The line of best fit passes through (0, 0) and (25, 70).

gradient = (70 - 0) / (25 - 0) = 2.8 mm min^-1
area of tube cross-section = pi x 0.5^2 = 0.785 mm^2
volume rate = 2.8 x 0.785 = 2.2 mm^3 min^-1
= 2.2 x 10^-3 cm^3 min^-1  (1 cm^3 = 1000 mm^3)

Intercepts. An intercept is where the line crosses an axis. Worked example: net oxygen release by an aquatic plant at different light intensities.

Light intensity / au	0	10	20	30	40	50
Net O₂ release / mm³ h⁻¹	−6.0	−3.1	0.1	3.1	5.9	9.2

The best-fit line crosses the y-axis at about −6.0 and the x-axis at about 20.

• The y-intercept is oxygen uptake with no light, which is respiration alone: 6.0 mm³ h⁻¹.
• The x-intercept is where net gas exchange is zero, the compensation point: about 20 au.
• The gradient is about 0.30 mm³ h⁻¹ au⁻¹.

Rate from a curve (tangent). The rate at a point on a curve is the gradient of the tangent there. Touch the curve with a ruler at that point only, draw the line long, then calculate its gradient.

Worked example. Volume of product against time in an enzyme reaction:

Time / s	0	10	20	30	40	50	60
Volume / cm³	0.0	6.6	11.0	14.0	16.0	17.3	18.2

A tangent at 30 s passes through (10, 9.2) and (50, 18.8).

rate at 30 s = (18.8 - 9.2) / (50 - 10) = 0.24 cm3 s^-1

The tangent at 0 s gives the initial rate, about 0.8 cm³ s⁻¹. The rate falls because substrate is used up, so initial rate is the best measure of enzyme activity.

1.1.4 Evaluation

(a) and (b) Results, conclusions and anomalies

An anomaly is a result that does not fit the pattern of the other repeats or the trend. Identify it by comparing with the repeats or the line of best fit. Exclude it from the mean and state why, or repeat the measurement, and suggest a cause such as a trapped bubble or a timing slip.

Worked example. Carbon dioxide from yeast in 5 minutes at 35 °C, five repeats: 12.4, 12.9, 12.6, 8.1, 12.8 cm³.

8.1 is anomalous (about 4.5 cm3 below the rest).
mean of the other four = (12.4 + 12.9 + 12.6 + 12.8) / 4 = 12.675 = 12.7 cm3
range of those four = 12.9 - 12.4 = 0.5 cm3
mean including 8.1 = 11.8 cm3, which is 7.2 % too low

(c) Limitations

A limitation is something about the procedure that reduces how far you can trust the result, such as a drifting water-bath temperature (rate affected by something other than the independent variable), only three values of the independent variable (optimum and trend shape unclear), reading a meniscus by eye (random scatter), starting the stopwatch late every time (the same error in every result, a systematic error) or a small sample (natural variation may hide or mimic a trend). Say what each does to the data.

(d) Precision, accuracy and uncertainty

• Precision: how close repeat measurements are to each other. A small range shows high precision. It does not show the values are correct.
• Accuracy: how close a measurement is to the true value.
• The uncertainty in a single reading is commonly taken as half the smallest division, so a syringe marked every 1 cm³ gives ±0.5 cm³.

Percentage uncertainty = (uncertainty ÷ measured value) × 100.

Percentage error = (measured value − accepted value) ÷ accepted value × 100, taken as a positive size if only the magnitude is asked for.

Worked example, percentage error. A 25.0 cm³ measuring cylinder delivers water of mass 24.3 g (1.00 g of water has volume 1.00 cm³).

percentage error = (25.0 - 24.3) / 25.0 x 100 = 2.8 %

Worked example, combining uncertainties. A gas syringe marked every 1 cm³ reads 2.0 cm³ at the start and 10.0 cm³ at 60 s. The stopwatch is read to ±0.5 s.

volume collected = 10.0 - 2.0 = 8.0 cm3
each reading is +/-0.5, so the difference is +/-1.0 cm3  (add absolute uncertainties)
volume: 1.0 / 8.0 x 100 = 12.5 %
time:   0.5 / 60 x 100 = 0.83 %
rate = 8.0 / 60 = 0.133 cm3 s^-1
percentage uncertainty in rate = 12.5 + 0.83 = 13.3 % (add percentage uncertainties for a quotient)
absolute uncertainty = 0.133 x 0.133 = 0.018, so rate = 0.133 +/- 0.018 cm3 s^-1

The volume reading is the main source. A syringe marked every 0.1 cm³ gives 0.1 ÷ 8.0 = 1.25 % instead.

(e) Improvements

Link each improvement to a specific limitation and say what it achieves.

Limitation	Improvement	Effect
Coarse syringe	Finer divisions, or collect a larger volume	Smaller percentage uncertainty
Temperature drift	Thermostatic bath, checked with a thermometer	Temperature controlled
Few pH values	Add values between 5 and 8	Optimum located more precisely
End point judged by eye	Colorimeter reading	Objective, quantitative
Natural variation	More organisms, calculate the mean	Individual differences averaged out

Common errors

• Naming a variable to control without saying how.
• Calling a boiled-enzyme tube a control variable.
• Treating precision as accuracy.
• Using one reading’s uncertainty for a difference of two readings.
• Writing “human error” instead of a specific cause.

Official syllabus

OCR Level 3 Advanced GCE in Biology A (H420), Version 4.1, April 2026, for first assessment in 2025, published by OCR. Section 1.1 and the mathematical requirements in section 5d.