Problems
Critics of p-values point out that the criterion used to decide "statistical significance" is based on the somewhat arbitrary choice of level (often set at 0.05). If significance testing is applied to hypotheses that are known to be false in advance, a non-significant result will simply reflect an insufficient sample size. The definition of "more extreme" data depends on the intentions of the investigator; for example, the situation in which the investigator flips the coin 100 times has a set of extreme data that is different from the situation in which the investigator continues to flip the coin until 50 heads are achieved.
There are also other problems associated with P-value: First, although only conducting the study or research once, P-values are interpreted in terms of long-term frequency, which does not make perfect sense; Second, P-values can be impacted by possibilities that never actually occurred; Third, only by changing the way the hypothesis testing question is asked, it would be highly possible to get different P-values from exactly the same data.
As noted above, the p-value p is the main result of statistical significance testing. Fisher proposed p as an informal measure of evidence against the null hypothesis. He called on researchers to combine p in the mind with other types of evidence for and against that hypothesis, such as the a priori plausibility of the hypothesis and the relative strengths of results from previous studies. Many misunderstandings concerning p arise because statistics classes and instructional materials ignore or at least do not emphasize the role of prior evidence in interpreting p. A renewed emphasis on prior evidence could encourage researchers to place p in the proper context, evaluating a hypothesis by weighing p together with all the other evidence about the hypothesis.
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