P-valueThe P-value is often used to make a decision regarding the validity of the null hypothesis based on the testing results. It represents the probability that the change is due to random, inherent sources. It is the chance of being wrong when it is decided to reject the null hypothesis.
Most often there are three test statistics used in hypothesis testing The calculated test statistic (depending on which comparison test is being used) is compared to the critital statistic of that same distribution (such as Z, t, F) to decided whether to "reject" or "fail to reject" the null hypothesis. The formulas for each test statistics are shown in the hypothesis testing section where there is more information on using the P-value in statistical tests.
IF... the calculated test statistic is > than the critical test statistic (found from a table), THEN... the decision is to reject the null hypothesis.
Using this method requires a calculation on the degrees of freedom, which requires only knowing the sample size(s). Another method of making a decision in a hypothesis test is by comparing the calculated P-value to the Alpha Risk .
If the P-value is > Alpha Risk, fail to reject the Ho (null hypothesis)
If the P-value is < Alpha Risk, reject the Ho (null hypothesis)and the data can be assumed to form a normal distribution.
Recall: Level of Confidence = 1 - Alpha Risk and this site uses 95% (Alpha Risk = 0.05) throughout.
Why P = 0.05?Click here to read an excellent description on the reasoning why 0.05 was selected as the general criteria to determine statistical significance.
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