False Discovery Rate

The False Discovery Rate is a method for conceptualizing the rate of Type I errors in null hypothesis testing when performing multiple comparisons. It focuses on the proportion of rejected null hypotheses that are actually true, providing a more flexible approach than controlling the family-wise error rate.

In quantitative finance, this allows researchers to accept a small number of false positives in exchange for discovering a larger number of genuine market signals. This is particularly useful when scanning across thousands of cryptocurrency pairs for arbitrage opportunities.

By controlling this rate, analysts can balance the trade-off between sensitivity and specificity in their model discovery processes. It is a standard practice in modern high-dimensional data analysis.