We're Using a Common Statistical Test All Wrong. Statisticians Want to Fix That
03-11-2016
The ASA has announced six principles for the use and interpretation of p-values. They say that p-values are widely misused. The following are the six principles from the ASA's statement:
- P-values can indicate how incompatible the data are with a specified statistical model.
- 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.
- Scientific conclusions and business or policy decisions should not be based only on whether a p-value passes a specific threshold.
- Proper inference requires full reporting and transparency.
- A p-value, or statistical significance, does not measure the size of an effect or the importance of a result.
- By itself, a p-value does not provide a good measure of evidence regarding a model or hypothesis.
Read this article explaining the six principles for the use and interpretation of p-values.