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TL;DR

A framework for deciding between a null hypothesis \( H_0 \) and alternative \( H_1 \) using sample data. The test statistic's distribution under \( H_0 \) determines whether to reject, based on a significance level \( \alpha \).

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

Hypothesis Testing

A framework for deciding between a null hypothesis \( H_0 \) and alternative \( H_1 \) using sample data. The test statistic's distribution under \( H_0 \) determines whether to reject, based on a significance level \( \alpha \).

Why it matters for interviews

Quant researchers use hypothesis testing to evaluate trading strategies: is the observed Sharpe ratio statistically significant or due to chance? Understanding Type I/II errors prevents overfitting and data snooping.

Definition and Mathematical Foundation

A framework for deciding between a null hypothesis \( H_0 \) and alternative \( H_1 \) using sample data. The test statistic's distribution under \( H_0 \) determines whether to reject, based on a significance level \( \alpha \).

Application in Quantitative Finance

Quant researchers use hypothesis testing to evaluate trading strategies: is the observed Sharpe ratio statistically significant or due to chance? Understanding Type I/II errors prevents overfitting and data snooping.

Related Terms

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Frequently Asked Questions

What are Type I and Type II errors?▼
Type I: rejecting a true null (false positive, probability = \( \alpha \)). Type II: failing to reject a false null (false negative, probability = \( \beta \)). Power = \( 1 - \beta \) is the probability of correctly detecting a true effect.
What is multiple testing correction?▼
Testing many strategies inflates false discovery rate. Bonferroni divides \( \alpha \) by the number of tests. Benjamini-Hochberg controls FDR more powerfully. This is critical in quantitative strategy backtesting.
How do you test if a trading strategy has positive alpha?▼
Regress strategy returns on factor returns. Test if the intercept (alpha) is significantly different from zero using a t-test. Account for autocorrelation (Newey-West standard errors) and multiple testing.