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4. Power of a Statistical Test
A term often used in clinical research is ‘statistical power’. The power of a statistical test is the probability that it will correctly lead to the rejection of a null hypothesis (H0) when it is false– i.e. the ability of the test to detect an effect, if the effect actually exists. Statistical power is inversely related to beta or the probability of making a type II error. In short, power = 1 – β.
In some cases we may not be able to reject the null hypothesis, not because it’s true, but because we do not have sufficient evidence against it. This might be because the experiment is not sufficiently large to reject H0. As such, the power of a test can be described as the probability of not making a type II error (not rejecting the null hypotheses when in fact it false).
Statistical power is affected chiefly by the size of the effect and the size of the sample used to detect it. Bigger effects are easier to detect than smaller effects, while large samples offer greater test sensitivity than small samples