GCSE Biology (AQA)

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Correlation & Causation

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Organisation Animal Tissues, Organs and Organ Systems

Correlation & Causation

3:31 Disease, Risk Factors and Sampling
Spec 4.2.2.5 4.2.2.6
Previous Risk Factors Next Effects of Diet & Exercise
  • Correlation and causation are different concepts in data analysis.
  • Correlation indicates an association between two variables, but does not imply one causes the other.
  • Example given: correlation between sunburn cases and ice cream sales, which is due to sunny weather rather than ice cream causing sunburn.
  • Correlations can be described as weak or strong, and positive or negative.
  • Weak correlation example: scattered data with a slight upward trend.
  • Strong correlation example: data closely aligned, forming a clear upward trend.
  • Negative correlation example: as one variable increases, the other decreases, e.g., eating more food reduces hunger.
  • Causation implies that one variable directly affects another, demonstrated through mechanisms or proven effects.
  • Example of causation: watering a plant leads to its growth.
  • Smoking and lung cancer correlation shown with data from 1950 to 2000, indicating a causal link as chemicals in cigarettes cause cancer in lab animals.
  • Importance of distinguishing between correlation and causation in interpreting data related to risk factors and diseases.

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