00:01
The power of a statistical test is the probability that will correctly reject a false null hypothesis.
00:08
In other words, it's the ability of a test to detect a true effect or difference when one exists.
00:14
Mathematically, power is equal to 1 minus the probability of a type ii error, which is a false negative.
00:21
A higher power indicates a greater likelihood of detecting a real effect if it's present.
00:26
The relationship between power and the probability of a type ii error is complementary.
00:33
As power increases, the probability of making a type ii error decreases.
00:37
Conversely, as a power decreases, the likelihood of failing to detect a true effect, which is a type ii error, increases.
00:44
So two actions to ensure adequate power would be to increase sample size...