Various functionals of are used as test statistics.

This estimate makes use of the fact that the genes whose test statistics fall in the quartile range will be predominantly the unchanged ones.

Fisher's method is typically applied to a collection of independent test statistics, usually from separate studies having the same null hypothesis.

In order to assess this, similar test statistics should be compared.

When the test statistics were not available, given a P value, we computed the corresponding test statistic from tables for the normal distribution.

"Evaluating test statistics to select interesting genes in microarray experiments."

In contrast to the Bonferroni correction it exploits the correlation between the test statistics for these comparisons.

It can also be used in the formulation of test statistics, such as the Wald test.

The test statistics should be identified by type, and given along with the p value, the sample size and the degrees of freedom when appropriate.

The critical values of the test statistics are shown below: