P Value

The probability value (p-value) of a statistical hypothesis test is the probability of getting a value of the test statistic as extreme as or more extreme than that observed by chance alone, if the null hypothesis Ho, is true.

It is the probability of wrongly rejecting the null hypothesis if it is in fact true.

It is equal to the significance level of the test for which we would only just reject the null hypothesis. The p-value is compared with the desired significance level of our test and, if it is smaller, the result is significant. That is, if the null hypothesis were to be rejected at the 5% significance level, this would be reported as “p < 0.05".

Small p-values suggest that the null hypothesis is unlikely to be true. The smaller it is, the more convincing the evidence is that null hypothesis is false. It indicates the strength of evidence for say, rejecting the null hypothesis H0, rather than simply concluding “Reject Ho” or “Do not reject Ho”.

——————————-
From “P-Value Of 0.05, 95% Confidence” Forum Message:
The p-value is basically the percentage of times you would see the value of the second mean IF the two samples are the same (ie from the same population). The comparison then is in the risk you are willing to take in making a type I error and declaring the population parameters are different. If the p-value is less than the risk you are willing to take (ie <0.05) then you reject the null and state that with a 95% level of confidence that the two parameters are not the same. If on the other hand, the p-value is greater than the risk you are assuming, you can only tell that there isn’t enough difference within the samples to conclude a difference. Where you set your risk level (alpha) then determines what p-value is significant.

See P-Value