Correlation vs causation. I find this is an issue that is quite simple, from a technical point of view, but widely misunderstood. Statistical significance does not imply causation. Correlation implies there may be a direct or indirect relationship, but does not imply causation. In fact, very few things imply causation. My simple version of the differences is below.
If you want to know why this is far more than a stoush to be had in an academic tea room, check out Tyler Vigen’s collection. If the age of Miss America can be significantly and strongly correlated with murders by steam, hot vapours and objects; then in any practical analysis there are many options for other less obvious spurious correlations. In a big data context, knowing the difference could be millions of dollars.
Occasionally, people opine that causation vs correlation doesn’t matter (especially in a big data and sometimes a machine learning context). I’d argue this is completely the wrong view to take: just because you have all the power that matters doesn’t mean we should ignore these issues because a randomised control trial is impractical in a lot of ways. It just means deciding when, how and why you’re going to do so in the knowledge of what you’re doing. Spurious correlations are common, hard to detect and difficult to deal with. It’s a bear hunt worth setting out on.