AQA GCSE Health & disease data (Biology)
Health & Disease data
This section is about handling and understanding data in the context of health and disease. You need to be able to work with data just like scientists do when studying how diseases spread or affect people.
Translate disease incidence information between graphical and numerical forms
Disease incidence = how often a disease occurs (e.g., number of new cases per 1,000 people).
You should be able to:
1.Read numbers from a graph (like a bar chart or line graph) and describe them in words or numbers.
2.Take a table of data and draw it as a graph or chart.
3.Switch between graphs and numbers easily.
If you’re given a table showing flu cases per month, you should be able to:
Draw a bar chart of the data.
If you’re shown a bar chart, you should be able to say how many flu cases there were in March.
Sample data in a table and bar chart is shown below.
Construct and interpret frequency tables and diagrams, bar charts and histograms
You should know how to:
Make frequency tables (how many times each value or category appears).
Draw and read:
Bar charts → used for categories (e.g., “smokers” vs “non-smokers”).
Draw and read:
Histograms → used for continuous data (e.g., ages, heights, or blood pressure ranges).
You might be asked to draw a histogram showing the distribution of people’s blood pressures.
Use a scatter diagram to identify a correlation between two variables
A scatter diagram (scatter graph) shows how two things might be related.
You could plot:
Exercise per week on the x-axis
Resting heart rate on the y-axis
If the points slope downwards, it shows a negative correlation — more exercise tends to mean a lower heart rate.
You don’t have to prove cause and effect — just recognise if there’s a relationship.
Understand the principles of sampling as applied to scientific data, including epidemiological data
Sampling = collecting data from a small group to represent a larger population.
In biology, this is used when studying populations or diseases.
You should understand:
What makes a good sample (large enough, random, representative). Typically around 10% of total population.
Why biased or small samples can give misleading results. This is because anomalous results can significantly affect the mean.
That epidemiological data = data about diseases in populations (e.g., how many people get a disease, risk factors, patterns, etc.).
If scientists only surveyed one school about asthma, it might not represent the whole country — that’s a biased sample.
Practice Questions
1.State what is meant by disease incidence
2. Describe what is meant by a positive correlation
3. Describe what is meant by the term sampling.