Graphs (AQA A-Level Biology): Revision Notes
Working With Graphs & Charts
Constructing graphs
- Graph layout: Ensure your graph occupies more than half the available space on the page
- Axis scaling: Mark axes using a ruler and divide them clearly with regular intervals (such as 10, 20, 30, 40 rather than 10, 15, 20, 30, 45)
- Variable placement: Plot the dependent variable (what you measured) on the y-axis and the independent variable (what you changed) on the x-axis
- Labelling: Include full titles and units for both axes. Write "pH of solution" rather than just "pH", or "mean height/m" rather than just "height".
- Plotting points: Use a sharp pencil and small 'x' marks so exact positions are clear and won't be obscured by trend lines.
- Best-fit lines: Draw smooth curves or straight ruled lines that show the overall trend. The line doesn't need to pass through every point, as biological data often shows natural variation.
- Range limits: Never extend your line beyond the measured data range. Extrapolation beyond your measurements is scientifically invalid and can lead to incorrect conclusions.
- Additional elements: Include a clear title, distinguish multiple trend lines with a key, and consider using broken axes when data starts far from zero.
Representing data uncertainty with error indicators
Range bars show the simplest form of data spread. They display the maximum and minimum values from repeat measurements, extending vertically from these extreme points through the plotted mean.
Error bars provide more sophisticated uncertainty representation using standard deviation - a calculated measure of data spread. Error bars extend one standard deviation above and below the mean, showing the mathematical variability rather than just extreme values.
Error bars are symmetrical and indicate measurement confidence. Longer error bars suggest greater data spread and less certainty about the true mean value. This statistical approach reduces the influence of extreme outliers that might affect range bars.
Calculating rates from biological graphs
Rate of change represents how quickly one variable changes relative to another, calculated as the gradient of the graph.
Worked Example: Calculating Rate from Straight-Line Graphs
- Select any two points on the plotted line
- Use a ruler to mark construction lines to both axes
- Measure the y-axis difference (dy) between the points
- Measure the x-axis time difference (dx) between the points
- Calculate dy ÷ dx and include appropriate units (such as cm³ O₂ per minute)
Worked Example: Calculating Rate from Curved Lines
- Draw a tangent line touching the curve at the steepest point (maximum rate)
- Select two points on this tangent line
- Follow the same calculation steps as for straight lines
This technique allows you to determine rates for biological processes like oxygen production during photosynthesis or bacterial population growth.
Analysing relationships with scatter diagrams
A scatter diagram plots two variables to identify potential correlations between them. The dependent variable goes on the y-axis, independent variable on the x-axis. After plotting points, you can add a trend line to visualise any relationship.
Biological examples include plotting lung cancer incidence against cigarettes smoked per day. Once plotted, the relationship strength can be tested using statistical methods like calculating the correlation coefficient (r).
Key Points to Remember:
- Percentage error decreases as measured values increase, so larger measurements are more reliable
- Choose graph types based on whether your independent variable is continuous (line graph) or categorical (bar chart/histogram)
- Never extrapolate beyond your measured data range when drawing trend lines
- Error bars using standard deviation provide better uncertainty representation than simple range bars