2. Correlation vs Causation

When analysing the results from a trial it is important to remember that correlation is not the same thing as causation. Correlation is when two variables are linked in some way however this does not mean that one will cause the other. An example of this involves hormone replacement therapy (HRT) and coronary heart disease (CHD) where women taking HRT were at less risk from CHD. This however was not due to the actual HRT process but rather due to the fact that the group of people receiving HRT tended to belong to a higher socio-economic group, with better-than-average diet and exercise regime. This is why it is important to record as much information as possible about the subjects participants of trials.

Therefore: To avoid being misled, approach correlations between variables with skepticism by looking for confounding factors. Humans like neat, causal narratives, but that’s usually not what the data is telling us.