Jumping to conclusions
Take care with correlation
So you’ve collected your data and noticed a strong correlation between two of your variables. It might be very tempting to assume that a change in one is causing a change in the other, but don’t fall into the trap. A correlation shows that there is a relationship between your variables; it doesn’t prove there is a causal relationship.
Think about Andrew Wakeﬁeld’s 1998 claim that using the MMR vaccine can result in autism. It’s true that use of the MMR vaccine had increased up to that point, as had the number of cases of autism recorded, so there’s a correlation between the two. This doesn’t necessarily mean that the jab is causing the increase in autism – there could be a third factor (called a confounder) causing one or both of the variables to increase, such as an increase in maternal age. Wakeﬁeld’s work has been discredited and he was struck oﬀ in 2010, meaning he can no longer practise as a doctor.
True causation can only be tested with carefully controlled studies, which often compare two groups who are matched in every way except for the variable of interest. This limits the role of confounders as much as possible. Oh, and watch your ‘u’s and ‘s’s – a causal relationship is very different from a casual one!Lead image: