AP Statistics: Chapter 21

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Presentation transcript:

AP Statistics: Chapter 21 More About Tests and Intervals: Notes

If we use the results of a significance test to make a decision, then we either reject the null hypothesis in favor of the alternative hypothesis, or we fail to reject the null hypothesis. We hope that our decision will be correct, but it is possible that we make the wrong decision. There are two ways to make a wrong decision: We can reject the null hypothesis when in fact it is true. This is called a Type I Error . We can fail to reject the null hypothesis when in fact it is false. This is called a Type II Error .

Truth about the population H0 is TRUE H0 is FALSE Decision based on sample REJECT H0 Type I Error p = a Correct Decision (Power) p = 1 – b FAIL TO REJECT H0 Correct Decision Type II Error p = b

We are interested in knowing the probability of making a Type I Error and the probability of making a Type II Error. A Type I Error occurs if we reject the null hypothesis when it is in fact true . When do we reject the null hypothesis? When we assume that it is true and find that the statistic of interest falls within the rejection region . The probability that the statistic falls in the rejection region is the area of the shaded region, or  .

Therefore the probability of a Type I Error is equal to the significance level  of a fixed level test. The probability that the test will reject the null hypothesis H0 when in fact H0 is true is  .

A Type II Error occurs if we fail to reject the null hypothesis when it is in fact false . When do we fail to reject the null hypothesis? When we assume that it is true and find that the statistic of interest falls outside the rejection region . However, the probability that the statistic falls outside the rejection region is NOT the area of the unshaded region. Think about it… If the null hypothesis is in fact false, then the picture is NOT CORRECT… it is off center.

To calculate the probability of a Type II Error, we must find the probability that the statistic falls outside the rejection region (the unshaded area) given that the mean is some other specified value.

The probability of a Type II Error tells us the probability of failing to reject the null hypothesis when it is actually false . The complement of this would be the probability of rejecting the null hypothesis when it is actually false. To calculate the probability of rejecting the null hypothesis when it is actually false, compute 1 – P(Type II Error). This is called the power of a significance test.

Unhappily single (Type I Error) Unhappily Married (Type II Error) Ho: x = the right person for me Ha: x = not the right person for me H0 is TRUE H0 is FALSE REJECT H0 Unhappily single (Type I Error) Happily single FAIL TO REJECT H0 Happily married Unhappily Married (Type II Error) 1?1?

False Alarm (Type I Error) Airport Scanners: Ho: x = the person is harmless (is not a terrorist) Ha: x = the person is not harmless (is a terrorist) H0 is TRUE H0 is FALSE REJECT H0 False Alarm (Type I Error) Terrorist is caught FAIL TO REJECT H0 Everybody is happy A Miss (Type II Error)