Table I.

The ASA statement's six principles on statistical signficance and P-values.

1	P-values can indicate how incompatible the data are with a specific 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 the result.
6	By itself, a P-value does not provide a good measure of evidence regarding a model or hypothesis.