Power refers to the probability that your test will find a statistically significant difference when such a difference actually exists. … It is generally accepted that power should be . 8 or greater; that is, you should have an 80% or greater chance of finding a statistically significant difference when there is one.

What is a good power for a statistical test?

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.

What statistical power is enough?

Power is usually set at 80%. This means that if there are true effects to be found in 100 different studies with 80% power, only 80 out of 100 statistical tests will actually detect them. If you don’t ensure sufficient power, your study may not be able to detect a true effect at all.