• sensitivity of a binary hypothesis test
• Probability that the test correctly rejects the null hypothesis H0H0 when the alternative is true H1H1
• Ability of a test to detect an effect, if the effect actually exists
• Power=P(rejectH0|H1istrue)Power=P(rejectH0|H1istrue)
• As power increases, chances of Type II error (false negative) decrease
• Used in the design of experiments, to calculate the minimum sample size required so that one can reasonably detects an effect. i.e: “how many times do I need to flip a coin to conclude it is biased?”
• Used to compare tests. Example: between a parametric and a non-parametric test of the same hypothesis

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