A conditional approach to inference after model selection
statistics
reproducibility
selective inference
R
Model selection can invalidate inference, such as significance tests, but statisticians have recently made progress developing methods to adjust for this bias. This post motivates a conditional approach with a simple screening rule example and introduces an R package that can compute adjusted significance tests.
Model selection bias invalidates significance tests
statistics
reproducibility
People often do regression model selection, either by hand or using algorithms like forward stepwise or the lasso. Sometimes they also report significance tests for the variables in the chosen model. But there’s a problem: the reason for p-value significance may just be something called model selection bias.
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