[From the introductory editorial “Moving to a World Beyond ‘p < 0.05’” by Ronald Wasserstein, Allen Schirm and Nicole Lazar, published in The American Statistician]

“Some of you exploring this special issue of The American Statistician might be wondering if it’s a scolding from pedantic statisticians lecturing you about what not to do with p-values, without offering any real ideas of what to do about the very hard problem of separating signal from noise in data and making decisions under uncertainty. Fear not. In this issue, thanks to 43 innovative and thought-provoking papers from forward-looking statisticians, help is on the way.”

“There’s not much we can say here about the perils of p-values and significance testing that hasn’t been said already for decades… If you’re just arriving to the debate, here’s a sampling of what not to do:”

— “Don’t base your conclusions solely on whether an association or effect was found to be “statistically significant” (i.e., the pvalue passed some arbitrary threshold such as p < 0.05).”

– “Don’t believe that an association or effect exists just because it was statistically significant.”

– “Don’t believe that an association or effect is absent just because it was not statistically significant.”

– “Don’t believe that your p-value gives the probability that chance alone produced the observed association or effect or the probability that your test hypothesis is true.”

– “Don’t conclude anything about scientific or practical importance based on statistical significance (or lack thereof).”

…

“The editors of this special TAS issue on statistical inference asked all the contact authors to help us summarize the guidance they provided in their papers by providing us a short list of do’s. We asked them to be specific but concise and to be active— start each with a verb.”

“Here is the complete list of the authors’ responses, ordered as the papers appear in this special issue. …”

To read more, click here.