ASA Statement on P-Values

This rarely happens (actually, this specific type of thing has actually never happened), but the American Statistical Association formed a committee and published a statement on P-Values.

Basically, P-Values have come under attack in recent years and many scattered discussions took place debating a few aspects of them and current practice. The ASA decided it would be helpful to centralize and organize thoughts a little and explain the most common pitfalls in current P-Value mentalities, since some folks haven’t yet fully understood these issues, and some folks have over-reacted to them.

The ASA boiled their thoughts down to six principles:

1- P-values can indicate how incompatible the data are with a specified statistical model.
2- P-values do not measure the probability that the studied hypothesis is true, or the
probability that the data were produced by random chance alone.
3- Scientific conclusions and business or policy decisions should not be based only on
whether a p-value passes a specific threshold.
4- Proper inference requires full reporting and transparency.
5- A p-value, or statistical significance, does not measure the size of an effect or the
importance of a result.
6- By itself, a p-value does not provide a good measure of evidence regarding a model or

The statement is aimed towards, and should be read in more detail, by anyone involved in research today. I share the opinion that p-values are like cars. Very useful, but you really shouldn’t use one without a license.

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