Correlations & Causal Relationships - The Lungs (AQA A Level Biology)

• Correlation is an association or relationship between variables
• Causation occurs when one variable has an influence or is influenced by another
• There is a clear distinction between correlation and causation: a correlation does not necessarily imply a causative relationship

Recognising Correlations and Causal Relationships

• Scientists present their findings from experiments in graphical and numerical forms to identify if there are relationships between risk factors and certain disease
• Scatter diagrams are used to identify correlations between two variables to determine if a factor (such as obesity) does increase the risk of developing a disease (such as type 2 diabetes)
  • In this example, BMI would be on the x-axis and Incidence of Type 2 Diabetes on the y-axis
  • It is important to note that not everyone with a high BMI will develop Type 2 Diabetes, however more people with a high BMI will develop Type 2 Diabetes than those with low BMI

• Correlation can be positive or negative
  • Positive correlation: as variable A increases, variable B increases
  • Negative correlation: as variable A increases, variable B decreases

• If there is no correlation between variables the correlation coefficient will be 0

Image showing different types of correlation in scatter graphs

Risk factors & causal relationships for lung diseases

• A causal relationship has been proven for some risk factors relating to lung diseases
• It is proven that smoking is a major risk factor for developing lung diseases such as emphysema
  • The chemicals in cigarettes damage the lungs and airways, making individuals more prone to developing the disease
  • This has been verified by other scientific investigations that have linked the chemicals to the damage which in turn leads to the disease

• Risk factors that are more easily controlled and measured are more likely to have a proven causal relationship, as they can be investigated in a more scientific manner
  • For example, an individual’s exposure to smoking is much easier to quantify than their exposure to air pollution

• When analysing data and studies it is always important to remember that risk factors interact with each other
  • For example, a smoker with asthma is likely to suffer the associated negative health side effects more quickly than a smoker without asthma

• It is always important to remember that even though there is a correlation, this does not mean that there is a direct causal link
  • For example, in places with higher pollution, there may be more asthmatic individuals but this does not mean that pollution caused asthma as there are many other variables at play