1. Testing Hypotheses about a Population Proportion
Lecture 30
Sections 9.3
Wed, Oct 24, 2007

2. Summary 1. H0: p = 0.50 H1: p > 0.50 2.  = 0.05.
2.  = 0.05.
3. Test statistic:
4. z = (0.52 – 0.50)/ = 1.26.
5. p-value = P(Z > 1.26) =
6. Do not reject H0.
7. It is not true that more than 50% of live births are male.

3. Summary 1. H0: p = 0.50 H1: p > 0.50 2.  = 0.05.
2.  = 0.05.
3. Test statistic:
4. z = (0.52 – 0.50)/ = 1.26.
5. p-value = P(Z > 1.26) =
6. Do not reject H0.
7. It is not true that more than 50% of live births are male.
Before
collecting
data

4. Summary 1. H0: p = 0.50 H1: p > 0.50 2.  = 0.05.
2.  = 0.05.
3. Test statistic:
4. z = (0.52 – 0.50)/ = 1.26.
5. p-value = P(Z > 1.26) =
6. Do not reject H0.
7. It is not true that more than 50% of live births are male.
After
collecting
data

5. Case Study 11
Male births vs. female births.

6. Testing Hypotheses on the TI-83
The TI-83 has special functions designed for hypothesis testing.
Press STAT.
Select the TESTS menu.
Select 1-PropZTest…
Press ENTER.
A window with several items appears.

7. Testing Hypotheses on the TI-83
Enter the value of p0. Press ENTER and the down arrow.
Enter the numerator x of p^. Press ENTER and the down arrow.
Enter the sample size n. Press ENTER and the down arrow.
Select the type of alternative hypothesis. Press the down arrow.
Select Calculate. Press ENTER.

8. Testing Hypotheses on the TI-83
The display shows
The title “1-PropZTest”
The alternative hypothesis.
The value of the test statistic Z.
The p-value.
The value of p^.
The sample size.
We are interested in the p-value.

9. Case Study 12
A recent study has shown that moderate exercise helps reduce the risk of catching a cold.
53 subjects were assigned to an exercise group that did moderate exercise.
62 subjects did only stretching exercises.
In the first group, only 5 caught a cold.
In the second group, 20 caught a cold.

10. Case Study 12
Use the TI-83 to test the hypothesis that a person who gets moderate exercise has less than a 1 in 3 chance of catching a cold.

11. The p-Value Approach p-Value approach.
Compute the p-value of the statistic.
Report the p-value.
If  is specified, then report the decision.

12. Two Approaches for Hypothesis Testing
Classical approach.
Specify .
Determine the critical value and the rejection region.
See whether the statistic falls in the rejection region.
Report the decision.

13. Classical Approach H0 

14. Classical Approach H0  

15. Classical Approach H0   z c
Critical value

16. Classical Approach H0   z c
Acceptance Region
Rejection Region

17. Classical Approach H0   z c
Acceptance Region
Rejection Region

18. Classical Approach H0   Reject z c z Acceptance Regionc z
Acceptance Region
Rejection Region

19. Classical Approach H0   z c
Acceptance Region
Rejection Region

20. Classical Approach H0  Accept  z z c Acceptance Regionz c
Acceptance Region
Rejection Region

21. The Classical Approach
The seven steps
1. State the null and alternative hypotheses.
2. State the significance level.
3. Write the formula for the test statistic.
4. State the decision rule.
5. Compute the value of the test statistic.
6. State the decision.
7. State the conclusion.
(Do not compute the p-value.)

22. The Classical Approach
The seven steps
1. State the null and alternative hypotheses.
2. State the significance level.
3. Write the formula for the test statistic.
4. State the decision rule.
5. Compute the value of the test statistic.
6. State the decision.
7. State the conclusion.
(Do not compute the p-value.)
Before
collecting
data

23. The Classical Approach
The seven steps
1. State the null and alternative hypotheses.
2. State the significance level.
3. Write the formula for the test statistic.
4. State the decision rule.
5. Compute the value of the test statistic.
6. State the decision.
7. State the conclusion.
(Do not compute the p-value.)
After
collecting
data

24. Example of the Classical Approach
Test the hypothesis that there are more male births than female births.
Let p = the proportion of live births that are male.
Step 1: State the hypotheses.
H0: p = 0.50
H1: p > 0.50

25. Example of the Classical Approach
Step 2: State the significance level.
Let  = 0.05.
Step 3: Define the test statistic.

26. Example of the Classical Approach
Step 4: State the decision rule.
Find the critical value.
On the standard scale, the value z0 = cuts off an upper tail of area 0.05.
This is a normal percentile problem.
Use invNorm(0.95) on the TI-83 or use the table.
Therefore, we will reject H0 if z >
The decision rule

27. Example of the Classical Approach
Step 5: Compute the value of the test statistic.

28. Example of the Classical Approach
Step 6: State the decision.
Because z < 1.645, our decision is to accept H0.
Step 7: State the conclusion.
The proportion of male births is not greater than the proportion of female births.

29. Summary H0: p = 0.50 H1: p > 0.50  = 0.05. Test statistic:
Reject H0 if z >
Accept H0.
The proportion of male births is the same as the proportion of female births.

30. Case Study 12
Use the TI-83 and the classical approach to test the hypothesis that a person who gets moderate exercise has less than a 1 in 3 chance of catching a cold.