遅ればせながら・・・
American Statistical Association (ASA)ステートメント：
Principles
- 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.
他アプローチ
These include methods that emphasize estimation over testing, such as confidence, credibility, or prediction intervals; Bayesian methods; alternative measures of evidence, such as likelihood ratios or Bayes Factors; and other approaches such as decision-theoretic modeling and false discovery rates.
All these measures and approaches rely on further assumptions, but they may more directly address the size of an effect (and its associated uncertainty) or whether the hypothesis is correct.
AMERICAN STATISTICAL ASSOCIATION RELEASES STATEMENT ON STATISTICAL SIGNIFICANCE AND P-VALUES
Provides Principles to Improve the Conduct and Interpretation of Quantitative Science March 7, 2016
https://www.amstat.org/newsroom/pressreleases/P-ValueStatement.pdf
Statisticians issue warning over misuse of P values Policy statement aims to halt missteps in the quest for certainty.
http://www.nature.com/news/statisticians-issue-warning-over-misuse-of-p-values-1.19503
信頼区間・尤度比あたりに落ち着くのかな
decision theoretic modeling、FDR(False Discovery Rate)(*)あたりは今後学習対象として重要？
