TL;DR

The probability of observing a test statistic at least as extreme as the one computed, assuming the null hypothesis is true. A small p-value (typically < 0.05) suggests the null is unlikely.

By Valenke Exam Prep Team·Last updated 2026-06-03

P-Value

The probability of observing a test statistic at least as extreme as the one computed, assuming the null hypothesis is true. A small p-value (typically < 0.05) suggests the null is unlikely.

Why it matters for interviews

P-values are the standard metric for statistical significance in strategy evaluation. However, misuse (p-hacking, data snooping) is rampant. Understanding what p-values do and do not mean is critical.

Definition and Mathematical Foundation

The probability of observing a test statistic at least as extreme as the one computed, assuming the null hypothesis is true. A small p-value (typically < 0.05) suggests the null is unlikely.

Application in Quantitative Finance

Related Terms

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Frequently Asked Questions

What does a p-value NOT tell you?▼

It does not give the probability that the null is true. It does not measure effect size or practical significance. A small p-value with tiny effect size may be statistically but not economically significant.

What is p-hacking?▼

Manipulating analysis (trying many tests, removing outliers, adding controls) until p < 0.05. It produces false discoveries. Solutions: pre-registration, out-of-sample testing, and Bayesian methods.

What is the Jeffreys-Lindley paradox?▼

With a large enough sample, even tiny deviations from the null produce small p-values. A Bayesian analysis might still favor the null. This highlights fundamental differences between frequentist and Bayesian testing.