In an interaction analysis, the probability of a false-positive result increases as the correlation between our covariate and predictor increases, and as the effect of our covariate x predictor interaction increases. The extremely simple solution is to include all covariate-by-predictor interactions in your model!
Clustered data - such as multiple observations per individual, animal, or cell - are quite common in neuroscience research. Here I walk through an introduction to one approach to adjusting your analyses for clustering - block permutations and bootstrapping - that is widely applicable and makes very few assumptions.
Here is my analysis of 'what correlates with how well someone climbs?'.
In this series I try to convey a couple of insights about power for interactions in linear regressions. First, how to do a power analysis for a interaction in a linear regression (this post), then interpreting the effect size of a interaction (part 2), and finally thinking about how large (or small) an effect size it is reasonable to plan for (part 3).