## What’s the significance of 0.05 significance?

From the post:

Why do we tend to use a statistical significance level of 0.05? When I teach statistics or mentor colleagues brushing up, I often get the sense that a statistical significance level of α = 0.05 is viewed as some hard and fast threshold, a publishable / not publishable step function. I’ve seen grad students finish up an empirical experiment and groan to find that p = 0.052. Depressed, they head for the pub. I’ve seen the same grad students extend their experiment just long enough for statistical variation to swing in their favor to obtain p = 0.049. Happy, they head for the pub.

Clearly, 0.05 is not the only significance level used. 0.1, 0.01 and some smaller values are common too. This is partly related to field. In my experience, the ecological literature and other fields that are often plagued by small sample sizes are more likely to use 0.1. Engineering and manufacturing where larger samples are easier to obtain tend to use 0.01. Most people in most fields, however, use 0.05. It is indeed the default value in most statistical software applications.

This “standard” 0.05 level is typically associated with Sir R. A. Fisher, a brilliant biologist and statistician that pioneered many areas of statistics, including ANOVA and experimental design. However, the true origins make for a much richer story.

One of the best history/explanations of 0.05 significance I have ever read. Highly recommended!

In part because in the retelling of this story Carl includes references that will allow you to trace the story in even greater detail.

What is dogma today, 0.05 significance, started as a convention among scientists, without theory, without empirical proof, without any of gate keepers associated with scientific publishing of today.

Over time 0.05 significance has proved its utility. The question for you is what other dogmas of today rely on the chance practices of yesteryear?

I first saw this in a tweet by Kirk Borne.