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:

  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.

Read this article explaining the six principles for the use and interpretation of p-values.

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