For a type II error probability of , the corresponding statistical power is 1 − . For example, if experiment E has a statistical power of 0.7, and experiment F has a statistical power of 0.95, then there is a stronger probability that experiment E had a type II error than experiment F. This reduces experiment E's sensitivity to detect significant effects. However, experiment E is consequently more reliable than experiment F due to its lower probability of a type I error. It can be equivalently thought of as the probability of accepting the alternative hypothesis (formula_2) when it is true — that is, the ability of a test to detect a specific effect, if that specific effect actually exists. Thus,