OCR Specification focus:
‘Plot suitable graphs from experimental results, selecting and labelling axes with appropriate scales, quantities and units.’
Graphs are essential tools for visualising biological data, revealing patterns, relationships and trends. Clear graphs allow reliable interpretation and support valid scientific conclusions by presenting processed data logically, accurately and in a comparable format.
Plotting Graphs in Biological Investigations
Graphs present processed data to show relationships between variables generated in practical investigations. In most OCR A-Level Biology contexts, the independent variable (the variable changed by the experimenter) is placed on the x-axis, while the dependent variable (the variable measured) is plotted on the y-axis. Plotting graphs in a scientifically rigorous way enables meaningful comparisons and supports robust analysis.
Independent variable: The variable that is deliberately changed in an investigation.
A graph must be clear, labelled and appropriately scaled so patterns can be recognised without ambiguity or distortion. Careful plotting also reduces misinterpretation and allows further mathematical analysis, such as calculating gradients or identifying correlations.
Choosing Axes, Quantities and Units
Accurate graphing begins with correct axis selection and labelling. The quantity (what is being measured) and the unit (the standard measurement scale) must be stated in axis titles, for example: Rate of reaction (s⁻¹) or Time (s).
A correctly formatted scientific graph with clearly labelled axes and SI units, reinforcing the OCR requirement to display both quantity and unit on each axis. Source.
Dependent variable: The variable that is measured to observe the effect of changes to the independent variable.
To meet OCR expectations, each axis should:
show the full range of data without excessive empty space
increase in equal increments with a linear scale, unless a logarithmic scale is specifically required
A comparison of linear and logarithmic scales showing how linear axes can compress data while logarithmic axes spread values more clearly. This includes extra contextual detail not required by the syllabus but directly illustrates when a log scale may be appropriate. Source.
use standard SI units wherever possible (e.g. metres, seconds, grams, degrees Celsius)
Scientific graphs should avoid ambiguous or inconsistent labelling. The use of compound units such as cm³ min⁻¹ is encouraged where appropriate, as it communicates both quantity and its measurement scale clearly.
Selecting an Appropriate Scale
A suitable scale ensures that plotted points occupy at least half of the available graph paper, improving clarity. Scales should be easy to interpret, typically increasing in steps of 1, 2 or 5, so that intermediate values can be read accurately. A compressed or exaggerated scale risks misrepresenting the relationship between variables, while uneven intervals reduce readability and scientific precision.
When planning scales, students should:
begin at zero unless there is a justified reason not to
choose increments that allow every plotted value to fall exactly on a scale line where possible
avoid overly large numbers that force rounding errors
A well-chosen scale supports later data interpretation, particularly when identifying gradients, intercepts or general trends.
Plotting Data Points Accurately
Raw measurements must be processed before graphing, then plotted using sharp, fine marks such as small crosses. Data points must be placed with precision, matching the correct values on both axes. Each point represents a pair of related measurements, and an error in plotting undermines the reliability of any biological conclusion drawn from the graph.
After plotting, a line of best fit may be drawn when data show a continuous trend. This line should represent the overall pattern, with roughly equal numbers of points above and below. For biological data showing steady change, a smooth curve is appropriate, whereas for proportional relationships a straight line of best fit may be used.
Units, Symbols and Quantities in Biological Contexts
Correct units and scientific symbols communicate meaning and prevent misinterpretation. Quantities must match the biological context, such as light intensity, enzyme concentration, population size or oxygen uptake. Using consistent units also allows datasets to be compared and supports secondary processing, including rate calculations and statistical analysis.
To maintain clarity in graph presentation, OCR expects:
axis titles with both quantity and unit
no repetition of units on tick marks
consistent unit use across the entire graph and associated data tables
Where conversions are required, they must be performed before plotting to avoid mixing units on a single axis.
Biological Significance of Clear Graphs
Well-constructed graphs allow students to visualise relationships, identify correlations, and link patterns to biological explanations, such as limiting factors in photosynthesis, enzyme activity changes or population growth trends. Graphs support concise communication of processed data and form a key part of scientific reasoning in written evaluations.
By following OCR’s expectations on axes, scales, quantities and units, students produce graphs that are both scientifically rigorous and easy to interpret, ensuring that trends can be confidently connected to underlying biological principles.
FAQ
Starting at zero is preferred because it prevents exaggerating differences between data points. However, if all values fall within a narrow range far from zero, starting at a higher value may improve clarity.
If you do not start at zero, the break must be clearly shown, and the scale must still increase at regular intervals. The choice should never distort the relationship between variables.
Choose a scale that uses simple increments, such as 1, 2, or 5, even if some values do not fall exactly on major gridlines.
Minor gridlines can be used to help place awkward values.
The key priority is that the scale remains readable, consistent, and easy to interpret.
In most cases, you should plot the mean of repeated measurements because it reduces the visual impact of random error and makes trends clearer.
However, repeat data can also be represented using error bars to show variability, especially when comparing precision between conditions.
Most A-Level biological data changes in a continuous, proportional way suited to linear scales. These scales make it easier to compare changes over equal intervals.
Logarithmic scales are only used when values span several orders of magnitude or when exponential change needs to be visualised.
A graph may be unsuitable if it has:
Missing units
Irregular or misleading scales
Poorly labelled axes
Inaccurate plotting
No line of best fit
Practice Questions
Question 1 (2 marks)
A student plots a graph with the independent variable on the y-axis and forgets to include units on both axes. State two errors the student has made.
Question 1 (2 marks)
Award one mark for each valid point:
Independent variable should be on the x-axis (1 mark)
Units must be included in the axis labels (1 mark)
Question 2 (5 marks)
A biology student collects data on the rate of oxygen uptake in woodlice at different temperatures. The student is instructed to plot a line graph of the processed data. Describe how the student should correctly set up and present the graph, including axes, scales, quantities, and units. Explain why these features are important for interpreting the results.
Question 2 (5 marks)
Award up to five marks for the following points:
Independent variable placed on the x-axis and dependent variable on the y-axis (1 mark)
Both axes clearly labelled with quantity and correct units (1 mark)
Scale chosen to cover the full range of data and spaced at regular, sensible intervals (1 mark)
Data points plotted accurately and a suitable line of best fit drawn (smooth curve or straight line as appropriate) (1 mark)
Clear presentation enables patterns, trends, or relationships to be interpreted effectively (1 mark)
