According to one view of hypothesis testing, the significance level should be specified before any statistical calculations are performed. Then, when the probability (p) is computed from a significance test, it is compared with the significance level. The null hypothesis is rejected if p is at or below the significance level; it is not rejected if p is above the significance level. The degree to which p ends up being above or below the significance level does not matter. The null hypothesis either is or is not rejected at the previously stated significance level. Thus, if an experimenter originally stated that he or she was using the 0.05 significance level and p was subsequently calculated to be 0.042, then the person would reject the null hypothesis at the 0.05 level. If p had been 0.0001 instead of 0.042 then the null hypothesis would still be rejected at the 0.05 level. The experimenter would not have any basis to be more confident that the null hypothesis was false with a p of 0.0001 than with a p of 0.041. Similarly, if the p had been 0.051 then the experimenter would fail to reject the null hypothesis.