Introduction

Like a lot of people, I slogged through my first undergraduate classes in inferential statistics. I’m not talking here about the truly basic, everyday stats like averages, medians, and ranges. I’m talking about things you don’t commonly run into outside the classroom, like randomized block designs and the analysis of variance.

I hated it. I didn’t understand it. Assistant professors and textbooks inflicted formulas on us, formulas that made little sense. We were supposed to pump data through the formulas, but the results had mysterious names like “mean square within.” All too often, the formulas appeared to bear no relationship to the concept they were supposed to quantify. Quite a bit later I came to understand that those formulas ...

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