Processes | P-Value Operations | P-Value Adjustment

P-Value Adjustment
Many statistical hypothesis testing methods produce p-values. A p-value is the probability of observing a test statistic that 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. That is, if we assume that the null hypothesis is true and observe something very rare, then it is very likely that our assumption is false and the null hypothesis should be rejected. P-values are very often misinterpreted as the probability of the null hypothesis itself or as the probability of a false positive.
Due to the number of tests being performed and the manner in which the results are to be used, the p-values might require either adjustment or transformation in order to adequately control false positives or false discovery rates across all tests.
Analysis of genomics data can produce thousands of p-values. Under a global null hypothesis of no associations and assuming that the tests are mutually independent, the distribution of the p-values is uniform. The chance of observing one or more small p-values increases directly with the number of tests conducted. In order to control for global error rates, such as the Familywise Error Rate or the False Discovery Rate, during multiple, simultaneous comparisons, p-values often should be statistically adjusted to account for multiple testing. The P-value Adjustment process provides you with a variety of multiple-testing adjustment methods along with log-based transformations, which allow for the rapid and easy discernment of highly significant p-values.
What do I need?
One Input Data Set, containing the p-values of an arbitrary set of features (for example, adverse events, laboratory results, and so on), is needed for this process.
For detailed information about the files and data sets used or created by JMP Life Sciences software, see Files and Data Sets.
Running the P-Value Adjustment process generates an output data set (identified by the _pva suffix) by adding additional columns containing the adjusted p-values to the input data set. One new column is generated for each P-value variable specified on the dialog.