## What is multiple comparison problem in fMRI data analysis?

In statistics, the multiple comparisons, multiplicity or multiple testing problem occurs when one considers a set of statistical inferences simultaneously or infers a subset of parameters selected based on the observed values.

### 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.

#### When should you adjust for multiple comparisons?

It is emphasized that adjustments for multiple testing are required in confirmatory studies whenever results from multiple tests have to be combined in one final conclusion and decision. In case of multiple significance tests a note on the error rate that will be controlled for is desirable.

**How do you correct p values for multiple comparisons?**

The simplest way to adjust your P values is to use the conservative Bonferroni correction method which multiplies the raw P values by the number of tests m (i.e. length of the vector P_values).

**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.

## What is family wise type1 error?

The familywise error rate (FWE or FWER) is the probability of a coming to at least one false conclusion in a series of hypothesis tests . In other words, it’s the probability of making at least one Type I Error. The FWER is also called alpha inflation or cumulative Type I error.

### Do you need to correct for multiple correlations?

If correlation coefficients, there is no need to do any correction. In most cases Bonferroni is excessively conservative, and another p-value correction method will probably be better. I would say that for multiple correlations, a p-value correction is usually not done.

#### Why is multiple testing a problem?

What is the Multiple Testing Problem? If you run a hypothesis test, there’s a small chance (usually about 5%) that you’ll get a bogus significant result. If you run thousands of tests, then the number of false alarms increases dramatically.

**When should I correct for multiple comparisons?**

Corrections for multiple comparisons may not be needed if you make only a few planned comparisons. The term planned comparison is used when: You focus in on a few scientifically sensible comparisons rather than every possible comparison. The choice of which comparisons to make was part of the experimental design.

**How is benjamini-Hochberg calculated?**

Suppose researchers are willing to accept a 20% false discovery rate. Thus, to calculate the Benjamini-Hochberg critical value for each p-value, we can use the following formula: (i/20)*0.2 where i = rank of p-value.

## Why do we need to correct for multiple comparisons?

However, the probability of committing false statistical inferences would considerably increase when more than one hypothesis is simultaneously tested (namely the multiple comparisons), which therefore requires proper adjustment.

### How do you control a FWER?

Holmes showed that the FWER is controlled with the following algorithm: Compare p(i) with α/(m−i+1) α / ( m − i + 1 ) . Starting from i = 1, reject until p(i) is greater. The most significant test must therefore pass the Bonferroni criterion.