1. Formulating a hypothesis about the population
2. Collecting a sample of observations from the population
3. Calculating statistics based on the sample
4. Either accepting or rejecting the hypothesis based on a predetermined acceptance criterion

There are two type of errors associated with the mean testing:

Type 1 error (alpha error) - The probability that a hypothesis that is actually true will be rejected. The value of alpha is known as the significance level of the test.

Type 2 error (beta error) - The probability that a hypothesis that is actually false will be accepted. Type 2 errors are often plotted in an operating characteristics curve.

Name Format Preview (Click to enlarge)   Hypothesis Testing with Sample Stats - Population Mean Template Microsoft Excel Format   Hypothesis Testing with Sample Stats - Population Mean Completed Example Microsoft Excel Format   Hypothesis Testing with Sample Data - Population Mean Template Microsoft Excel Format   Hypothesis Testing with Sample Data - Population Mean Completed Example Microsoft Excel Format   Sample size Determination for Testing (Mu) Template Microsoft Excel Format   Sample size Determination for Testing Completed Example Microsoft Excel Format   Beta vs Alpha Testing (Type I and Type II Error probabilities) Template Microsoft Excel Format   Beta vs Alpha Testing (Type I and Type II Error probabilities) Completed Example Microsoft Excel Format   Power Curve for a (Mu) Test Template Microsoft Excel Format   Power Curve for a (Mu) Test Completed Example Microsoft Excel Format   Operating Characteristic Curve Test Template Microsoft Excel Format   Operating Characteristic Curve Test Completed Example Microsoft Excel Format

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