‘Correlation does not imply causation’ is a statistical mantra. Most good high school and undergraduate statistics courses teach this, and most good science bloggers, journalists and scientists repeat it over and over again. But when and how far does that mantra extend into regression model territory? And what of the no-man’s-land surrounding this mysterious terra statistica?

Causal language refers to definitive statements that describe a cause and effect between two variables. It is in the same camp as the active voice, which is increasingly being promoted as the ‘way to write’ for scientists. Passive voice and non-directional language, once the standard of scientific writing, are now seen by some as vague, ambiguous and open to misinterpretation. But in our rush to be active, confident and ‘own’ our research results, are we risking misinterpretation and misunderstanding of science at the other end of the scale? “Building more roads increases bee abundance” might sound dramatic, convincing and galvanising…but it doesn’t mean quite the same thing as “Bee abundance was associated with the number of grassy road verges in the landscape”.* Continue reading →