6 Princípios sobre significância estatística e p-valor

Recentemente, a Associação Americana de Estatística divulgou alguns princípios e condutas para a correta interpretação do p-valor:

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.

Cada item eh comentado neste artigo.

PS: depois volto pra escrever melhor sobre isso.

Fonte: 

The ASA's statement on p-values: context, process, and purpose
Published in
The American Statistician, March 2016
DOI 10.1080/00031305.2016.1154108
Authors
Ronald L. Wasserstein