1TJPC - Statistical Significance and P-Values
#p-values
#statistics
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
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
but note the first part is the editorial the second part is the statement.
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- Starts 01 June 2022 07:50 AM UTC
- Ends 07 June 2022 06:50 AM UTC
- No Admission Charge