P-Value Operations

Many statistical hypothesis testing methods produce p-values. A p-value is the probability of observing that a test statistic is as or more extreme than the one computed from the data assuming that the relevant null hypothesis is true. The null hypothesis usually represents no association or relationship, and so, smaller p-values represent more evidence that the null is not true. The scale of evidence here is based on a type of probabilistic modus tollens, or argument by contradiction. P-values are very often misinterpreted as the probability of the null hypothesis or as the probability of a false positive.

The P-Value Operations category menu provides a set of bioinformatic tools that can help you view, adjust, and combine p-values in preparation for more detailed analyses. Refer to the table below for guidance.

Process	Choose this process for...
P-Value Combination	Combining p-values from multiple units (such as SNPs or genes) into a single p-value for each group (such as a gene or pathway, respectively) that comprises those units
P-Value Adjustment	Adjusting p-values using multiple-testing methods and log-based transformations
P-Value Quantile Plotter	Graphically comparing observed p-values for a data set with those that might be expected under relevant null hypotheses
Meta-Analysis	Performing a meta-analysis of multiple studies by combining p-values or effects for a particular test from the studies and calculating a combined p-value

See the JMP Genomics Starter main page for other process categories.