OCR GCSE Biology A: Working scientifically: planning, recording, presenting and evaluating practical data (J247)

This guide covers the working scientifically section (5e) of the OCR GCSE (9-1) Gateway Science Biology A (J247) specification, Version 4.0 (August 2026): the practical skills in statements 2a to 2d, and the experimental, analysis and vocabulary statements 1.2, 1.3 and 1.4 as they are used in the Topic B7 practicals. None of these statements carries the Higher Tier only marking, so Foundation and Higher candidates need all of it. OCR says 15% of the questions in the written papers assess practical skills.

Related pages: the OCR GCSE Biology hub, the printable checklist, the revision notes, the practice questions, the apparatus and PAG 1 to 4 guide, the PAG 5 to 8 guide and the free 10-minute diagnostics.

What this unit covers

Statement	What you must be able to do
2a	Carry out experiments: manipulate apparatus correctly, measure accurately, follow written instructions, think about health and safety
2b	Make and record observations and measurements with a range of apparatus; keep appropriate records
2c	Present observations in descriptive, tabular, diagrammatic and graphical forms
2d	Communicate the rationale, methods, findings and reasoned conclusions in paper-based and electronic reports, using verbal, diagrammatic, graphical, numerical and symbolic forms
1.2a to 1.2e	Build a hypothesis from theory; plan; choose apparatus; sample representatively; evaluate and improve
1.3a to 1.3i	Present, translate and analyse data; mean, mode, median; distributions and uncertainty; interpret; explain; accuracy, precision, repeatability, reproducibility; random and systematic error; communicate
1.4a to 1.4f	Vocabulary, quantities, SI units, prefixes, unit conversion, significant figures

The worked data below are new for this page. Work each one through yourself before reading the answer.

1. From theory to hypothesis to plan (1.2a, 1.2b, 1.2c)

A hypothesis is a testable explanation built from scientific theory. A prediction is what you expect to see if it is right.

Worked example 1. Theory: plants need mineral ions, including nitrate, to make proteins, so a lack of nitrate limits growth.

• Hypothesis: radish seedlings given a nitrate solution grow longer roots than seedlings given only water.
• Prediction: after five days the mean root length in nitrate solution will be greater than in water.

A plan names every variable.

Type	Item	How handled
Independent	Nitrate concentration	Set at 0, 1, 2 and 4 g per dm³ by diluting a stock
Dependent	Root length	Measured in mm with a ruler, from the seed to the root tip
Control	Temperature, light, volume of solution, seed variety, seed size, time	Same dish, same shelf, same 10 cm³ of solution, same pack of seeds, five days

A fair plan also includes:

1. A control group: 0 g per dm³ (water only) shows what growth happens without the factor.
2. Enough seedlings: ten per concentration, because individuals vary.
3. Repeats: repeating the whole set gives repeatability.
4. Apparatus chosen for the job: a measuring cylinder or syringe for solution volumes (a beaker is not accurate enough), a ruler marked in millimetres for roots, a timer for the five days.
5. Risks: wash hands after handling seedlings and soil, and keep spilled solution off the floor.

Sampling (1.2d). A sample must represent the whole. Take seeds at random from the pack, not the biggest ones. In the field, use random positions (for example, random coordinates for quadrats) and enough of them. Too few samples can give a mean that is not typical.

2. Carrying out and recording (2a, 2b)

• Read the whole instruction sheet before you start, then follow it in order.
• Read a scale at eye level. For a liquid in a measuring cylinder, read the bottom of the meniscus at eye level.
• Give each reading the same number of decimal places, matching the resolution of the instrument. A ruler marked in mm is read to the nearest mm. Do not write 24.0 mm if you can only judge 24 mm.
• Record at the time, directly into a table, in ink or pencil as instructed, and never on scrap paper. Cross out mistakes with one line.
• Record qualitative observations too (colour, smell, appearance of seedlings), because they can explain odd results.

Table design. Name the independent variable in the first column and the dependent variable after it, with units in the headings only, not in the cells.

Nitrate (g per dm³)	Root length (mm) of seedling 1 to 10	Mean root length (mm)
0	21, 24, 23, 24, 26, 22, 24, 25, 23, 28	24.0
2	36, 38, 35, 34, 36, 37, 35, 61, 36, 37	see section 3

3. Averages, range and anomalies (1.3c, 1.3d)

Mean = total of values / number of values. Median is the middle value of the ordered list (the mean of the two middle ones for an even count). Mode is the most frequent value. Range is largest minus smallest.

Worked example 2. Control data from the table: 21, 22, 23, 23, 24, 24, 24, 25, 26, 28 (ordered).

• Total = 240, so mean = 240 / 10 = 24.0 mm.
• Median = (24 + 24) / 2 = 24 mm. Mode = 24 mm. Range = 28 - 21 = 7 mm.
• Uncertainty estimate: half the range = 3.5 mm, so the result is written 24.0 ± 3.5 mm.

Worked example 3. In the 2 g per dm³ set, 61 mm is far from the others. It is an anomalous result, perhaps a measuring or recording slip, perhaps a different kind of seed.

• With 61: total = 385, mean = 38.5 mm.
• Without 61 (nine values): total = 324, mean = 324 / 9 = 36.0 mm, median 36 mm, mode 36 mm, range 38 - 34 = 4 mm, so 36.0 ± 2.0 mm.
• Say what you did and why. Do not delete it silently, and do not drop a value just because it is inconvenient. If you can, repeat that measurement.
• The median is 36 mm with or without the 61, so it resists an anomaly better than the mean does.

Conclusion by comparison. Percentage change = (36.0 - 24.0) / 24.0 x 100 = 50%. Mean root length is 50% greater with nitrate. The two results, 24.0 ± 3.5 mm (up to 27.5) and 36.0 ± 2.0 mm (down to 34.0), do not overlap, which supports the hypothesis. Compare the spread, not only the means.

4. Quality of data (1.3g, 1.3h)

Term	Meaning
Accuracy	How close a measurement is to the true value
Precision	How close repeat measurements are to each other
Repeatability	The same person, method and equipment give the same results again
Reproducibility	A different person, method or equipment gives the same results
Random error	Unpredictable scatter above and below the true value; reduce by repeating and averaging
Systematic error	Every reading is shifted the same way (a zero error, a miscalibrated thermometer, always reading from above); repeating does not remove it

Worked example 4. The true volume delivered by a syringe is 10.0 cm³. Student A reads 10.1, 9.9, 10.0. Student B reads 11.4, 11.5, 11.4.

• A is precise (spread 0.2 cm³) and accurate (mean 10.0 cm³).
• B is precise (spread 0.1 cm³) but not accurate (mean 11.4 cm³). The error is the same each time, so it is systematic, for example a scale that reads about 1.4 cm³ too high.

5. Sampling in the field (1.2d, 1.3c)

Worked example 5. Twelve random 0.25 m² quadrats on a lawn give snail counts of 2, 3, 3, 4, 4, 4, 5, 5, 5, 5, 6, 2.

• Total = 48, mean = 48 / 12 = 4.0 snails per quadrat.
• Median: ordered list is 2, 2, 3, 3, 4, 4, 4, 5, 5, 5, 5, 6, so the middle two are both 4, median = 4. Mode = 5.
• Density = 4.0 / 0.25 = 16 snails per m².
• Estimate for a 400 m² lawn = 16 x 400 = 6400 snails.

This is an estimate, and it is only as good as the sample. More quadrats give a better estimate.

6. Presenting data (2c, 1.3a, 1.3b)

Choose the form to fit the data.

Data	Best form
Both variables are numerical and continuous	Line graph or scatter graph
Independent variable is a category (species, site)	Bar chart with gaps
Continuous data grouped into classes	Histogram or frequency table, bars touching
Shape or structure	Labelled diagram, drawn in pencil with ruled label lines
Patterns and observations	Written description

Graph rules: independent variable on the x axis; axes labelled with quantity and unit; a scale that uses over half the grid and goes up in even steps; points plotted as small crosses; a smooth line or line of best fit that need not go through every point or the origin; a title if there is more than one graph. A scatter graph that shows both variables rising together is a positive correlation. Correlation shows a link, not that one causes the other.

Translating data (1.3b). A graph can be turned back into a table or described in words: “as concentration increases, mean root length increases then levels off”.

7. Rate from a graph (M4 skills, 1.3e)

Worked example 6. Yeast in sugar solution produces carbon dioxide, collected in a gas syringe.

Time (min)	0	2	4	6	8	10
Volume (cm³)	0	3.1	6.0	9.2	12.1	15.0

Points form a straight line of best fit through (0, 0) and (10, 15.0).

• Gradient = change in volume / change in time = 15.0 / 10 = 1.5 cm³ per min.
• The equation is y = mx + c with c = 0, so V = 1.5t.
• At 15 minutes the line predicts 1.5 x 15 = 22.5 cm³. That is extrapolation. It assumes the sugar does not run out, so treat it as a prediction only.

8. Units, prefixes and magnification (1.4)

Prefix	Symbol	Factor
tera	T	10¹²
giga	G	10⁹
mega	M	10⁶
kilo	k	10³
deci	d	10⁻¹
centi	c	10⁻²
milli	m	10⁻³
micro	µ	10⁻⁶
nano	n	10⁻⁹

Magnification = size of image / size of real object, with both in the same unit.

Worked example 7. A drawing of a cell is 63 mm long. The cell is really 90 µm.

• Convert: 63 mm = 63 x 1000 = 63 000 µm.
• Magnification = 63 000 / 90 = x700.
• A second drawing at x700 is 48 mm long. Real length = 48 000 µm / 700 = 68.6 µm, which is 69 µm to 2 significant figures.
• In standard form, 90 µm = 9.0 x 10⁻⁵ m.

Give answers to a sensible number of significant figures, no more than the data justify.

9. Evaluating and communicating (1.2e, 1.3f, 1.3i, 2d)

When asked to evaluate, point to a weakness, say what effect it had, then give a specific improvement.

Worked example 8. The nitrate study kept the dishes on a window sill.

• Weakness: light and temperature varied between dishes, which are uncontrolled variables.
• Effect: differences in root length may be caused by these rather than by nitrate.
• Improvement: place all dishes on one shelf under the same lamp.
• Further investigation: test 0.5 and 8 g per dm³ to find the concentration where growth stops rising.

A conclusion must refer to the data, use words such as “mean” and “increased”, relate back to the hypothesis, and not claim more than the data show. State the rationale (why you did it), the method, the findings and the reasoned conclusion in a logical order.

Common errors

• Listing “temperature” as a control without saying how it is kept the same.
• Using a bar chart where both variables are continuous.
• Putting units in every table cell.
• Calling a result “accurate” when it is only precise.
• Averaging an anomaly into the mean without comment.
• Mixing mm and µm in a magnification calculation.

Official syllabus

OCR GCSE (9-1) Gateway Science Biology A (J247), Version 4.0 (August 2026), first assessment 2018, published by OCR: section 5e Working scientifically (statements 1.2, 1.3, 1.4 and 2a to 2d), the mathematical skills table in section 5f, and Topic B7 Practical skills.