How should we cope with the fact that data-driven processes, multiplicity and selection effects can invalidate a method's control of error probabilities?.Experts do not agree on how to improve reliability, and these disagreements reflect philosophical battles–old and new– about the nature of inductive-statistical evidence and the roles of probability in statistical inference. High powered methods, the big data revolution, and the crisis of replication in medicine and social sciences have prompted new reflections and debates in both statistics and philosophy about the role of traditional statistical methodology in current science. You have just fished for data.20221113T0900 20221113T1145 America/New_York Multiplicity, Data-Dredging, and Error Control You would like to show progress, so you decide to include them in your report to management. Your company has no business in Ohio or Louisiana, and it there is no reasonable conclusion why these correlations exist, even though the p-values are less than 5%.
Of the 100, two show statistical significance: interest rates on boat loans in south Louisiana and household debt in north Ohio.
You download a database from a government website and decide to run a series of automated regressions. Imagine that instead of choosing only four macroeconomic indicators, you chose 100. This could have gone another way…īUT wait! This could have gone another way.
An honest analyst would not make this claim, but show that there is no relationship. You had conflicting evidence of the statistical significance of two correlations, but since one side of the evidence supports a personal claim you would like to make, you decide to accept it. Since you’re in a bind, you decide to claim that GDP and Employment rate have a strong relationship to revenue, and that you’re going to explore this further.
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