One important caveat when working with large datasets is that you can almost always produce a statistically significant result when performing a null hypothesis test. This is why it is even more critical to evaluate the effect size than the p value in such an analysis. It is equally important to consider the distribution of your data, and its implications for statistical inference. In this blog post, I use simulated data in order to explore this caveat more intuitively, focusing on a pre-print article that was recently featured on BBC.
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