Fdr

What Fdr is

Fdr (False Discovery Rate) is a method for controlling the rate of false positives when performing multiple comparisons in a given data set. The Fdr algorithm is a way to measure the rate of false discoveries in the context of multiple hypothesis testing. It is used to control the probability of falsely rejecting the null hypothesis for any individual test.

Steps for Fdr:

  1. Identify the null hypothesis and the alternative hypothesis.

  2. Calculate the p-value for each hypothesis test.

  3. Sort the p-values from smallest to largest.

  4. Calculate the Fdr rate for each p-value.

  5. Compare the calculated Fdr rate to a predetermined threshold.

  6. Reject the null hypothesis if the Fdr rate is greater than the threshold.

Examples

  1. Fdr is used to identify false discovery rate in multiple hypothesis testing.
  2. Fdr is used to control the probability of falsely rejecting the null hypothesis in multiple hypothesis testing.
  3. Fdr is used to adjust the p-values of multiple hypothesis tests to account for the fact that multiple tests are being conducted.
  4. Fdr is used to estimate the expected rate of false discoveries among the rejected hypotheses in multiple hypothesis testing.

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