Do We Really Need the S-word?
The use of “significance” in reporting statistical results is fraught with problems—but they could be solved with a simple change in practice
Join the S-word Movement
In my experience, scientists making their first attempts at abandoning the s-word discover how wedded they are to it. The real challenge, however, lies in replacing the s-word with substance, not with an equally ambiguous synonym. If the scenario is a simple one—the p-value was 0.048, the confidence interval did not include 0 or the variable in question ended up in your top model—use the space to explicitly define your criteria. If, in fact, you believe the results are practically meaningful and important, convince your readers with sound justification using both statistical and general scientific reasoning.
Statistical inference is an art, uncomfortably dependent on practitioners and their backgrounds. It should not be construed as a way to objectivize inference or a straightforward means to classify results as significant or not. Omission of the s-word may seem like a rather insignificant request among the bigger issues facing statistical inference and science in general. However, given the simplicity and accessibility of this change, it is worth the potential improvements it offers in the dissemination of our scientific results. I hope you will join me and my students in working to curtail use of the s-word and its negative impacts on science.
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