Contents
What does p-value compare?
The p value is the evidence against a null hypothesis. The smaller the p-value, the stronger the evidence that you should reject the null hypothesis. When you run a hypothesis test, you compare the p value from your test to the alpha level you selected when you ran the test.
Can we compare two p values?
In your particular case there is absolutely no doubt that you can directly compare the p-values. If the sample size is fixed (n=1000), then p-values are monotonically related to t-values, which are in turn monotonically related to the effect size as measured by Cohen’s d. Specifically, d=2t/√n.
How to calculate p value for multiple t-tests?
My dataset consists of n genes, each of them described by a vector of expression values, 5 for “healthy” individuals, and 5 for “unhealthy” individuals. I am going to run n t-tests (one for each gene) to identify which genes show a different behaviour between healthy population and unhealthy population.
How to get p values from multiple comparisons?
Answer 3: Fisher’s Least Significant Differences. P values that don’t correct for multiple comparisons. Prism 6, but not earlier versions, can do this. An alternative to adjusted P values is to compute a P value (and confidence interval) for each comparison, without adjusting for multiple comparisons.
When to use same cut off for p value?
However, what becomes a critical issue is that the same cut-off is used when ‘multiple’ tests are undertaken on the same case-control (or any pairwise) comparison. Here, in brevity, we present what the P value represents, and why and when it should be adjusted.
When is a two tailed p value worth further study?
Prism offers two approaches to decide when a two-tailed P value is small enough to make that comparison worthy of further study following the multiple t tests (and nonparametric) analysis. One approach is based on the familiar idea of statistical significance.