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. 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. 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.