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## What is the best correction for multiple comparisons?

The most conservative of corrections, the **Bonferroni correction** is also perhaps the most straightforward in its approach. Simply divide α by the number of tests (m). However, with many tests, α* will become very small. This reduces power, which means that we are very unlikely to make any true discoveries.

## Do I need to correct for multiple comparisons?

**Some statisticians recommend never correcting for multiple comparisons while analyzing data** (1,2). Instead report all of the individual P values and confidence intervals, and make it clear that no mathematical correction was made for multiple comparisons. This approach requires that all comparisons be reported.

## 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 is the major problem of multiple comparisons?

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**. The more inferences are made, the more likely erroneous inferences become.

## What are multiple comparison methods?

Multiple comparison methods (MCMs) are **designed to investigate differences between specific pairs of means or linear combinations of means**. This provides the information that is of most use to the researcher.

## 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).

## Is Bonferroni correction necessary?

When conducting multiple analyses on the same dependent variable, the chance of committing a Type I error increases, thus increasing the likelihood of coming about a significant result by pure chance. **To correct for this, or protect from Type I error, a Bonferroni correction is conducted**.

## 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.

## How does multiple testing correction work?

Perhaps the simplest and most widely used method of multiple testing correction is the Bonferroni adjustment. If a significance threshold of α is used, but n separate tests are performed, then the Bonferroni adjustment deems a score significant only if the corresponding P-value is ≤α/n.

## How can the chance of committing a Type I error be reduced when performing multiple comparisons?

A Type I error is when we reject a true null hypothesis. Lower values of α make it harder to reject the null hypothesis, so **choosing lower values for α** can reduce the probability of a Type I error.

## How do benjamini Hochberg correction?

**Calculate each individual p-value’s Benjamini-Hochberg critical value, using the formula (i/m)Q, where:**

- i = the individual p-value’s rank,
- m = total number of tests,
- Q = the false discovery rate (a percentage, chosen by you).

## How do I use FDR correction in Excel?

*First we need to sort the p values in ascending. Order. Next we will assign rank for the samples. You just have to drag 2 until the very.*

## 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 FDR q-value?

q-value is **a widely used statistical method for estimating false discovery rate (FDR)**, which is a conventional significance measure in the analysis of genome-wide expression data. q-value is a random variable and it may underestimate FDR in practice.

## What is FDR correction?

The false discovery rate (FDR) is **a statistical approach used in multiple hypothesis testing to correct for multiple comparisons**. It is typically used in high-throughput experiments in order to correct for random events that falsely appear significant.

## What is FDR adjusted p-value?

The FDR is **the ratio of the number of false positive results to the number of total positive test results**: a p-value of 0.05 implies that 5% of all tests will result in false positives. An FDR-adjusted p-value (also called q-value) of 0.05 indicates that 5% of significant tests will result in false positives.

## Is p-value false positive rate?

A positive is a significant result, i.e. the p-value is less than your cut off value, normally 0.05. **A false positive is when you get a significant difference where, in reality, none exists**. As I mentioned above, the p-value is the chance that this data could occur given no difference actually exists.

## How do you protect against false positives?

- Avoid excessive testing (think before data exploration)
- Keep track of number of tests conducted and report all tests.
- Bonferroni correction, false-discovery rate or emphasize preliminary nature of findings.
- Average effect sizes across conceptually similar tests.

## What is the difference between false discovery rate and p-value?

**The false discovery rate is the complement of the positive predictive value (PPV) which is the probability that, when you get a ‘significant’ result there is actually a real effect**. So, for example, if the false discovery rate is 70%, the PPV is 30%.

## Is 0.039 statistically significant?

The P value of 0.039 is **not compelling evidence by itself**. The vaccine does not have a proven track record of significant results. The confidence interval indicates that that the estimated effect size is both small and imprecise.

## What does p-value of 0.05 mean?

The smaller the p-value, the stronger the evidence that you should reject the null hypothesis. A p-value less than 0.05 (typically ≤ 0.05) is **statistically significant**. It indicates strong evidence against the null hypothesis, as there is less than a 5% probability the null is correct (and the results are random).

## Why do we use 0.05 level of significance?

For example, a significance level of 0.05 **indicates a 5% risk of concluding that a difference exists when there is no actual difference**. Lower significance levels indicate that you require stronger evidence before you will reject the null hypothesis.

## Is p-value of 0.1 significant?

The smaller the p-value, the stronger the evidence for rejecting the H_{0}. This leads to the guidelines of p < 0.001 indicating very strong evidence against H_{0}, p < 0.01 strong evidence, p < 0.05 moderate evidence, p < 0.1 weak evidence or a trend, and **p ≥ 0.1 indicating insufficient evidence**[1].

## What is the difference between 0.01 and 0.05 level of significance?

Different levels of cutoff trade off countervailing effects. **Lower levels – such as 0.01 instead of 0.05 – are stricter, and increase confidence in the determination of significance, but run an increased risk of failing to reject a false null hypothesis**.

## How do you test the hypothesis at 0.05 level of significance?

To graph a significance level of 0.05, we need to **shade the 5% of the distribution that is furthest away from the null hypothesis**. In the graph above, the two shaded areas are equidistant from the null hypothesis value and each area has a probability of 0.025, for a total of 0.05.