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

hypothesis.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.