Model selection bias invalidates significance tests

statistical modeling
Significance testing may be one of the most popular statistical tools in science. Researchers and journals often treat significance–having a p-value < 0.05<0.05–as indication that a finding is true and perhaps publishable. But the tests used to compute many of the p-values people still rely on today
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