It’s all too easy to make mistakes involving statistics. Statistical software can remove a lot of the difficulty surrounding statistical calculation, reducing the risk of mathematical errors, but correctly interpreting the results of an analysis can be even more challenging.

A few years ago, Minitab trainers compiled a list of common statistical mistakes, the ones they encountered repeatedly. Being somewhat math-phobic myself, I expected these mistakes would be primarily mathematical. I was wrong: Every mistake on their list involved either the incorrect interpretation of the results of an analysis, or a design flaw that made meaningful analysis impossible.

Here are three of their most commonly observed mistakes that involve drawing an incorrect conclusion from the results of analysis. (I’m sorry to say that, yes, I have made all three of these mistakes at least once.)

Mistake No. 1: Not distinguishing between statistical significance and practical significance

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