9.2 Outcomes and the Type I and Type II errors

Type I error

Probability of making a type I error: P(type I error)= a a= 0.05 thus a 5% chance of concluding an effect exists when it doesn't in the population. P(not making type 1 error)= 1-a 95% chance of not making a type I error.

Type II error

Probability of making a type II error: P(type II error) = B B is difficult to determine, because we frequently don't know true population values. Probability of not making a type II error: P (not making a type II error) = 1-B. Statistical power= 1- B.

a

probability of a type I error= P(type I error)= probability of rejecting the null hypothesis when the null hypothesis is true.

B

probability of a type II error= P(type II error)= probability of not rejecting the null hypothesis when the null hypothesis is false.

The Power of The Test

1-B

What are the 4 possible outcomes?

1. The decision is not to reject H0 when H0 is true (correct decision) 2. The decision is to reject H0 when H0 is true (incorrect decision known as a Type 1 error) 3. The decision is not to reject H0 when, in fact, H0 is false (incorrect decision known as a Type II error). 4. The decision is to reject H0 when H0 is false (correct decision whose probability is called the Power of the Test).

How many outcomes are there when you perform a hypothesis test?

4. Depending on the actual truth (or falseness) of the null hypothesis H0 and the decision to reject or not.

What are the greek letters that represent the probabilities?

a and B

Statistical power

is the ability to detect an edict when it does exist in the population.