P-value

In statistical hypothesis testing, the p**-value** is the probability of obtaining a test statistic at least as extreme as the one that was actually observed, assuming that the null hypothesis is true.1(http://en.wikipedia.org/wiki/P-value#cite_note-Dict-1) One often "rejects the null hypothesis" when the p-value is less than the significance level α (Greek alpha), which is often 0.05 or 0.01.

Although there is often confusion, the p-value is not the probability of the null hypothesis being true, nor is the p-value the same as the Type I error rate.2(http://en.wikipedia.org/wiki/P-value#cite_note-hubbard-2) A Type I error in statistics is the incorrect rejection of the null hypothesis. In this case the hypothesis was correct but wrongly rejected. In a Type II error, however, the null hypothesis was not rejected despite being incorrect. This results in the failure of rejection of incorrect assumptions.

From <http://en.wikipedia.org/wiki/P-value>