1. Copyright © 2010 Pearson Education, Inc. Slide 22 - 1 Beware: Lots of hidden slides!

2. Copyright © 2010 Pearson Education, Inc. Slide 22 - 2

3. Copyright © 2010 Pearson Education, Inc. Slide 22 - 3 Answer: A

4. Copyright © 2010 Pearson Education, Inc. Chapter 22 Comparing Two Proportions

5. Copyright © 2010 Pearson Education, Inc. Slide 22 - 5 Comparing Two Proportions Comparisons between two percentages are much more common than questions about isolated percentages. And they are more interesting. We often want to know how two groups differ, whether a treatment is better than a placebo control, or whether this year’s results are better than last year’s.

6. Copyright © 2010 Pearson Education, Inc. Slide 22 - 6 The Standard Deviation of the Difference Between Two Proportions Proportions observed in independent random samples are independent. Thus, we can add their variances. So… The standard deviation of the difference between two sample proportions is Thus, the standard error is

7. Copyright © 2010 Pearson Education, Inc. Slide 22 - 7 Example: A recent survey of 886 randomly selected teenagers (age 12-17) found that more than half of them had online profiles. Some researchers and privacy advocates are concerned about the possible access to personal information about teens in public places on the internet. There appear to be differences between boys and girls in their online behavior. Among teens aged 15- 17, 57% of the 248 boys had posted profiles, compared to 70% of the 256 girls. Let’s start the process of estimating how large the true gender gap might be. What is the standard error of the difference in sample proportions?

8. Copyright © 2010 Pearson Education, Inc. Slide 22 - 8 Assumptions and Conditions Independence Assumptions: Randomization Condition: The data in each group should be drawn independently and at random from a homogeneous population or generated by a randomized comparative experiment. The 10% Condition: If the data are sampled without replacement, the sample should not exceed 10% of the population. Independent Groups Assumption: The two groups we’re comparing must be independent of each other.

9. Copyright © 2010 Pearson Education, Inc. Slide 22 - 9 Assumptions and Conditions (cont.) Sample Size Condition: Each of the groups must be big enough… Success/Failure Condition: Both groups are big enough that at least 10 successes and at least 10 failures have been observed in each.

10. Copyright © 2010 Pearson Education, Inc. Slide 22 - 10 Sample: Among randomly sampled teens aged 15-17, 57% of the 248 boys had posted online profiles, compared to 70% of the 256 girls. Can we use these results to make inferences about all 15-17 year-olds?

11. Copyright © 2010 Pearson Education, Inc. Slide 22 - 11 The Sampling Distribution We already know that for large enough samples, each of our proportions has an approximately Normal sampling distribution. The same is true of their difference.

12. Copyright © 2010 Pearson Education, Inc. Slide 22 - 12 The Sampling Distribution (cont.) Provided that the sampled values are independent, the samples are independent, and the samples sizes are large enough, the sampling distribution of is modeled by a Normal model with Mean: Standard deviation:

13. Copyright © 2010 Pearson Education, Inc. Slide 22 - 13 Two-Proportion z-Interval When the conditions are met, we are ready to find the confidence interval for the difference of two proportions: The confidence interval is where The critical value z* depends on the particular confidence level, C, that you specify.

14. Copyright © 2010 Pearson Education, Inc. Slide 22 - 14 Sample: Among randomly sampled 15-17 year olds, 57% of the 248 boys had posted online profiles, compared to 70% of the 256 girls. We calculated the standard error for the difference in sample proportions to be.0425 and found that the assumptions and conditions required for inference were met. What does a confidence interval say about the difference in online behavior?

15. Copyright © 2010 Pearson Education, Inc. Slide 22 - 15 TI-Tips Find a Confidence Interval STATS TESTS B:2-PropZInt 57% of the 248 boys = 141.36 (round to nearest int.) 70% of the 256 girls = 179.2 (round to nearest int.) (Put data in, largest %, then smallest %)

16. Copyright © 2010 Pearson Education, Inc. Slide 22 - 16 Hypothesis Test for the Difference in Two Proportions The typical hypothesis test for the difference in two proportions is the one of no difference. In symbols, H 0 : p 1 – p 2 = 0. Since we are hypothesizing that there is no difference between the two proportions, that means that the standard deviations for each proportion are the same. Since this is the case, we combine (pool) the counts to get one overall proportion.

17. Copyright © 2010 Pearson Education, Inc. Slide 22 - 17 Everyone into the Pool (cont.) The pooled proportion is where and If the numbers of successes are not whole numbers, round them first. (This is the only time you should round values in the middle of a calculation.)

18. Copyright © 2010 Pearson Education, Inc. Slide 22 - 18 We then put this pooled value into the formula, substituting it for both sample proportions in the standard error formula:

19. Copyright © 2010 Pearson Education, Inc. Slide 22 - 19 Compared to What? We’ll reject our null hypothesis if we see a large enough difference in the two proportions. How can we decide whether the difference we see is large? Just compare it with its standard deviation. Unlike previous hypothesis testing situations, the null hypothesis doesn’t provide a standard deviation, so we’ll use a standard error (here, pooled).

20. Copyright © 2010 Pearson Education, Inc. Slide 22 - 20 Two-Proportion z-Test The conditions for the two-proportion z-test are the same as for the two-proportion z-interval. We are testing the hypothesis H 0 : p 1 – p 2 = 0, or, equivalently, H 0 : p 1 = p 2. Because we hypothesize that the proportions are equal, we pool them to find

21. Copyright © 2010 Pearson Education, Inc. Slide 22 - 21 Two-Proportion z-Test (cont.) We use the pooled value to estimate the standard error: Now we find the test statistic: When the conditions are met and the null hypothesis is true, this statistic follows the standard Normal model, so we can use that model to obtain a P-value.

22. Copyright © 2010 Pearson Education, Inc. Slide 22 - 22 I think it is best to learn by example… Example of a two-proportion z-test: One concern of the study on teens’ online profiles was safety and privacy. In the random sample, girls were less likely than boys to say that they are easy to find online from their profiles. Only 19% (62 girls) of 325 teen girls with profiles say that they are easy to find, while 28% (75 boys) of the 268 boys with profiles say the same. Are these results evident of a real difference between boys and girls? Perform a two—proportion z-test and discuss what you find.

23. Copyright © 2010 Pearson Education, Inc. Slide 22 - 23 TI-Tips Two-proportion z-test Stat Tests 6:2-PropZTest Enter numbers for each group Choose tail. Determines the z-value and p-value!

24. Copyright © 2010 Pearson Education, Inc. Slide 22 - 24 Example (#24): A survey of 430 randomly chosen adults found that 21% of the 222 men and 18% of the 208 women had purchased books online. a. Is there evidence that men are more likely than women to make online purchases of books? Test an appropriate hypothesis and state your conclusion in context. b. If you conclusion in fact proves to be wrong, did you make a type I or Type II error? c. Estimate this difference with a confidence interval. d. Interpret your interval in context.

25. Copyright © 2010 Pearson Education, Inc. Slide 22 - 25 What Can Go Wrong? Don’t use two-sample proportion methods when the samples aren’t independent. These methods give wrong answers when the independence assumption is violated. Don’t apply inference methods when there was no randomization. Our data must come from representative random samples or from a properly randomized experiment.

26. Copyright © 2010 Pearson Education, Inc. Slide 22 - 26 Pg. 519 1-15 (day 1) Pg. 519 16-25 (day2)