  In contrast to its common definition and calculation, interpretation of
p-values diverges among statisticians. Since p-value is the basis of various
methodologies, this divergence has led to a variety of test methodologies and
evaluations of test results. This chaotic situation has complicated the
application of tests and decision processes. Here, the origin of the divergence
is found in the prior probability of the test. Effects of difference in Pr(H0 =
true) on the character of p-values are investigated by comparing real
microarray data and its artificial imitations as subjects of Student's t-tests.
Also, the importance of the prior probability is discussed in terms of the
applicability of Bayesian approaches. Suitable methodology is found in
accordance with the prior probability and purpose of the test.
