Not to be confused with the P-factor. Korner fourier analysis pdf article includes a list of references, but its sources remain unclear because it has insufficient inline citations.
Please help to improve this article by introducing more precise citations. The use of p-values in statistical hypothesis testing is common in many fields of research such as physics, economics, finance, political science, psychology, biology, criminal justice, criminology, and sociology.
Their misuse has been a matter of considerable controversy. The p-value is used in the context of null hypothesis testing in order to quantify the idea of statistical significance of evidence.
Null hypothesis testing is a reductio ad absurdum argument adapted to statistics. In essence, a claim is assumed valid if its counter-claim is improbable.
A result is said to be statistically significant if it allows us to reject the null hypothesis. That is, as per the reductio ad absurdum reasoning, the statistically significant result should be highly improbable if the null hypothesis is assumed to be true.
The rejection of the null hypothesis implies that the correct hypothesis lies in the logical complement of the null hypothesis. However, unless there is a single alternative to the null hypothesis, the rejection of null hypothesis does not tell us which of the alternatives might be the correct one.
However, supposing we manage to reject the zero mean hypothesis, even if we know the distribution is normal and variance is unity, the null hypothesis test does not tell us which non-zero value we should adopt as the new mean. Thus, this naive definition is inadequate and needs to be changed so as to accommodate the continuous random variables. Example of a p-value computation.