Simply put, power is the probability of not making a Type II error, according to Neil Weiss in Introductory Statistics. Mathematically, power is 1 – beta. The power of a hypothesis test is between 0 and 1; if the power is close to 1, the hypothesis test is very good at detecting a false null hypothesis.

How do you interpret power in statistics?

Power is the probability that a test of significance will pick up on an effect that is present. Power is the probability that a test of significance will detect a deviation from the null hypothesis, should such a deviation exist.

What does a statistical power of 0.8 mean?

Scientists are usually satisfied when the statistical power is 0.8 or higher, corresponding to an 80% chance of concluding there’s a real effect.