This month Andrew Smith will be discussing the “Statement on Statistical Significance and P-Values” by the American Statistical Association.

Key points will be:

– 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.

from the press release: [https://www.amstat.org/asa/files/pdfs/p-valuestatement.pdf](https://protect-au.mimecast.com/s/BbOGCp8A57TnpRpBiPwoDG?domain=amstat.org)

this statement is available here:

Ronald L. Wasserstein & Nicole A. Lazar (2016) the ASA Statement on p-Values: Context, Process, and Purpose, The American Statistician, 70:2, 129-133, DOI: 10.1080/00031305.2016.1154108

[https://doi.org/10.1080/00031305.2016.1154108](https://protect-au.mimecast.com/s/BHMVCq7B4Du8JMJofXZtZr?domain=doi.org)

but note the first part is the editorial the second part is the statement.

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