16 Hypothesis Testing
Hypothesis testing is one of the cornerstones of statistical inference, used widely across disciplines such as economics, finance, psychology, and more. Researchers employ hypothesis testing to draw conclusions about population parameters based on sample data. Central to this process is the concept of the p-value, which helps quantify how unlikely the observed data (or more extreme data) would be if the null hypothesis were true.
However, as data collection has become easier and cheaper—especially in the age of big data—there is a growing awareness that large sample sizes (large \(n\)) can inflate the likelihood of finding statistically significant, but practically negligible, effects. Moreover, this can lead to “p-value hacking,” where researchers run numerous tests or adopt flexible analytical approaches until they find a (sometimes minuscule) effect that achieves a conventional significance level (often \(p < .05\)).
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