p-value cannot determine importance of results

this is refreshing news via nature.com in the morning. the American Statistical Association (ASA) warns that the P value “CANNOT determine whether a hypothesis is true or whether results are important.” (emphasis added).

a 6-principle list that addresses misconceptions and misuse of the p-value, can be found in the ASA 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.

the press release statement goes here:
Screenshot 2016-03-08 07.47.15

good to take note for those of us into educational research, and have the tendency to rely on quanti methods. and its our duty to correct the misconceptions of non-researchers on the use of p-value too.