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Type I error
To incorrectly reject H₀
(actual significance level)

Type II error
To incorrectly accept H₀

Size
Probability of rejecting H₀, when it’s true
Smaller size = better test

Power
Probability of rejecting H₀, when it’s false
Larger power = better test

Power function
Power of the test written algebraically
Uses of the power function
With a graph, you can find where the power is the largest
Compare hypothesis tests, the one with the larger power is a better test
Check if the power of a test is above 0.5 at a specific probability or lambda value