Statisticians say the darnedest things. At least, that’s how it can seem if you’re not well-versed in statistics.

When I began studying statistics, I approached it as a language. I quickly noticed that, compared to other disciplines, statistics has some unique problems with terminology. These are problems that don’t affect most scientific and academic specialties.

For example, dairy science has a highly specialized vocabulary, which I picked up when I was an editor at Penn State’s College of Agricultural Sciences. I found the jargon fascinating, but not particularly confusing to learn. Why? Because words like “rumen” and “abomasum” and “omasum” simply don’t turn up in common parlance. They have very specific meaning, and there’s little chance of misinterpreting them.

Now open up a statistics text and flip to the glossary. There are plenty of statistics-specific terms, but you’re going to see a lot of very common words as well. The problem is that in statistics, these common words don’t necessarily mean what they do outside statistics.

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