The False Discovery Rate: Its Meaning, Interpretation and Application in Data Science

The False Discovery Rate

An essential tool for statisticians and data scientists seeking to interpret the vast troves of data that increasingly power our world

First developed in the 1990s, the False Discovery Rate (FDR) is a way of describing the rate at which null hypothesis testing produces errors. It has since become an essential tool for interpreting large datasets. In recent years, as datasets have become ever larger, and as the importance of ‘big data’ to scientific research has grow...