# What is FDR correction?

## What is FDR correction?

The false discovery rate (FDR) is a statistical approach used in multiple hypothesis testing to correct for multiple comparisons. Therefore, a multiple testing correction, such as the FDR, is needed to adjust our statistical confidence measures based on the number of tests performed.

## What is the difference between p-value and FDR?

Another way to look at the difference is that a p-value of 0.05 implies that 5% of all tests will result in false positives. An FDR adjusted p-value (or q-value) of 0.05 implies that 5% of significant tests will result in false positives. The latter will result in fewer false positives.

**Why is it important to correct for multiple comparisons in the analysis of fMRI data?**

In fMRI research, the goal of correcting for multiple comparisons is to identify areas of activity that reflect true effects, and thus would be expected to replicate in future studies.

**What does FDR of 1 mean?**

false discovery rate

It stands for the “false discovery rate” it corrects for multiple testing by giving the proportion of tests above threshold alpha that will be false positives (i.e., detected when the null hypothesis is true).

### What is local FDR?

The local FDR (fdr) is the probability that the hypothesis comes from the null at a specific value of the statistic.

### What does an FDR of 1 mean?

**What does FDR 1 mean?**

**What is benjamini Hochberg method?**

The Benjamini–Hochberg method controls the False Discovery Rate (FDR) using sequential modified Bonferroni correction for multiple hypothesis testing.

## What is the multiple comparisons problem and why is it a problem in neuroimaging?

The problem is that we don’t know which one! In neuroimaging, the problem is exaggerated because we have so many more comparisons. Let’s say we have 10,000 voxels in our image and use a cutoff of p < . 05 (uncorrected), and find to our delight that we have 500 significant voxels.