There are a lot of different ways to think about comparing two things, or more appropriately perhaps, two sets of things. If they are things we can count, we can easily see which there are more of. If they are more like a score, we can easily see which set has a higher score. We can also fairly easily see what the distribution of the things in each set are, although comparing the distributions is a bit more tricky.

Using some basic statistics measures, we can tell whether or not the two sets of things are different from each other using significance testing. This is typically done with a t-test or an analysis of variance (ANOVA) or a similar measure. These types of measures, based on the mean and variance of a set of data points, are simple and easy to calculate (especially with a basic stats program) and have therefore become commonplace in the research literature. But unfortunately, their simplicity ends up hiding a lot of information and potentially interesting nuance.

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