A century ago, two oddly domestic puzzles helped set the rules for what modern science treats as "real": a Guinness brewer charged with quality control and a British lady insisting she can taste ...
One FAQ released this week discusses the validation of employee selection procedures and, most notably, how OFCCP will assess the validity of selection tools built on AI and other “‘new technology’ ...
It’s too often misused and misunderstood. by Amy Gallo When you run an experiment or analyze data, you want to know if your findings are “significant.” But business relevance (i.e., practical ...
In order to prevent misleading conclusions based on spurious observed effects--especially seductively large ones--Robinson and Levin (1997) suggested a two-step approach to the reporting and ...
The perennial question facing all banks is, “What volume of lending in Majority-Minority census tracts and mortgages extended to minority home borrowers must we do to avoid redlining allegations by ...
It may be common knowledge that p < .05 indicates statistical significance. Psychology students (and others) are often taught that p < .05 means the probability (p) of rejecting the null hypothesis ...
It may be common knowledge that p < .05 indicates statistical significance. Psychology students (and others) are often taught that p < .05 means the probability (p) of rejecting the null hypothesis ...
Citations: McShane, Blake, Andrew Gelman. 2022. Selecting on Statistical Significance and Practical Importance Is Wrong. Journal of Information Technology. (3)312-315.
Instead of recognizing the limitations of statistical significance, fields including economics and medicine ossified around it, with dire consequences for science. In the 21st century, an obsession ...
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