The False Discovery Rate

Its Meaning, Interpretation and Application in Data Science

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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 grown, the significance of the FDR has grown correspondingly. The False Discovery Rate provides an analysis o...