*p* Value - the probability of observing a test statistic that is as extreme or more extreme than currently observed *assuming that the null
hypothesis is true. *This can be expressed as Pr(data|*H*_{0}), where "Pr" is read "the probability of" and "|" is read as "given" or
"conditional upon." The statistic should NOT be interpreted as the probability of *H*_{0} being true.

**Use**

*p* Value can be used in different ways. Using a fixed-level method of hypothesis testing, we compare the *p* value to a predetermined a.
If *p* < a, the null hypothesis is rejected. This provides a method of "inductive behavior" intended to restrict the number of type I errors
a researcher will make in the long run. Fixed-level testing was promoted by Pearson and Neymann as a framework decision making
but has been criticized as "unscientific" according to some points of view.

*p* Value can also be used more flexibly if we adopt Fisher's philosophy of significance testing. Using Fisher's approach the *p* value is as
a measure of evidence from a single experiment. As a measure of evidence, the *p* value is meant to be combined with other sources of
information. Thus, there is no set threshold for "significance" (Fisher, 1973).

The researchers should *not *place reliance on the *p* value as a means of reaching causal conclusions. The *p* values must be interpreted in
context of other information.