1. Copyright © 2014, 2011 Pearson Education, Inc. 1 Chapter 16 Statistical Tests

2. Copyright © 2014, 2011 Pearson Education, Inc. 2 16.1 Concepts of Statistical Tests A manager is evaluating software to filter SPAM e-mails (cost $15,000). To make it profitable, the software must reduce SPAM to less than 20%. Should the manager buy the software?  Use a statistical test to answer this question  Consider the plausibility of a specific claim (claims are called hypotheses)

3. Copyright © 2014, 2011 Pearson Education, Inc. 3 16.1 Concepts of Statistical Tests Null and Alternative Hypotheses  Statistical hypothesis: claim about a parameter of a population.  Null hypothesis (H 0 ): specifies a default course of action, preserves the status quo.  Alternative hypothesis (H a ): contradicts the assertion of the null hypothesis.

4. Copyright © 2014, 2011 Pearson Education, Inc. 4 16.1 Concepts of Statistical Tests SPAM Software Example Let p = email that slips past the filter H 0 : p ≥ 0.20 H a : p < 0.20 These hypotheses lead to a one-sided test.

5. Copyright © 2014, 2011 Pearson Education, Inc. 5 16.1 Concepts of Statistical Tests One- and Two-Sided Tests  One-sided test: the null hypothesis allows any value of a parameter larger (or smaller) than a specified value.  Two-sided test: the null hypothesis asserts a specific value for the population parameter.

6. Copyright © 2014, 2011 Pearson Education, Inc. 6 16.1 Concepts of Statistical Tests Type I and II Errors  Reject H 0 incorrectly (buying software that will not be cost effective)  Retain H 0 incorrectly (not buying software that would have been cost effective)

7. Copyright © 2014, 2011 Pearson Education, Inc. 7 16.1 Concepts of Statistical Tests Type I and II Errors indicates a correct decision

8. Copyright © 2014, 2011 Pearson Education, Inc. 8 16.1 Concepts of Statistical Tests Other Tests  Visual inspection for association, normal quantile plots and control charts all use tests of hypotheses.  For example, the null hypothesis in a visual test for association is that there is no association between two variables shown in the scatterplot.

9. Copyright © 2014, 2011 Pearson Education, Inc. 9 16.1 Concepts of Statistical Tests For Example, in a Normal Quantile Plot H 0 : Data are a sample from a normally distributed population There is only a 5% chance of any point lying outside limits. Data are close enough to line; we do not reject H 0

10. Copyright © 2014, 2011 Pearson Education, Inc. 10 16.1 Concepts of Statistical Tests Test Statistic  Statistical tests rely on the sampling distribution of the test statistic that estimates the parameter specified in the null and alternative hypotheses.  Key question: What is the chance of getting a test statistic this far from H 0 if H 0 is true?

11. Copyright © 2014, 2011 Pearson Education, Inc. 11 16.2 Testing the Proportion SPAM Software Example Apparent savings of licensing the software depends on the sample proportion.

12. Copyright © 2014, 2011 Pearson Education, Inc. 12 16.2 Testing the Proportion SPAM Software Example  The analysis of profitability indicates the manager should reject H 0 and license the software only if is is small enough (less than a threshold).

13. Copyright © 2014, 2011 Pearson Education, Inc. 13 16.2 Testing the Proportion SPAM Software Example α Level  The threshold for rejecting H 0 depends on manager’s willingness to take a chance on licensing software that won’t be profitable  Based on the probability of making a Type I error (designated as α – level of significance)

14. Copyright © 2014, 2011 Pearson Education, Inc. 14 16.2 Testing the Proportion SPAM Software Example Sampling distributions (n=100) for different values of p. When p = 0.2, there are the most small values of ; therefore, α is set at 5% for this value of p (which is p 0 ).

15. Copyright © 2014, 2011 Pearson Education, Inc. 15 16.2 Testing the Proportion SPAM Software Example z-Test Assuming p=0.2, find the threshold C such that the probability that a sample with falls below it is 0.05 (shaded area is called rejection region).

16. Copyright © 2014, 2011 Pearson Education, Inc. 16 16.2 Testing the Proportion SPAM Software Example z-Test  P (Z < -1.645) = 0.05  Based on n=100 and SE( ) = 0.04 (note that the hypothesized value p 0 = 0.20 is used to calculate SE), then C = 0.2 – 1.645 (0.04) =.01342.

17. Copyright © 2014, 2011 Pearson Education, Inc. 17 16.2 Testing the Proportion z–Test for SPAM Software Example (review of 100 e-mails showed 12% spam) = -2

18. Copyright © 2014, 2011 Pearson Education, Inc. 18 16.2 Testing the Proportion SPAM Software Example  z-Test: test of H 0 based on a count of the standard errors separating H 0 from the test statistic.  The observed sample proportion is 2 standard errors below p 0. Since z < -1.645 the managers rejects H 0 ; the result is statistically significant.

19. Copyright © 2014, 2011 Pearson Education, Inc. 19 16.2 Testing the Proportion SPAM Software Example  p-Value: the smallest α level at which H 0 can be rejected.  Statistical software commonly reports the p-value of a test.

20. Copyright © 2014, 2011 Pearson Education, Inc. 20 16.2 Testing the Proportion SPAM Software Example The p-value is the area to the left of the observed statistic

21. Copyright © 2014, 2011 Pearson Education, Inc. 21 16.2 Testing the Proportion p–Value for SPAM Software Example Interpret the p-value as a weight of evidence against H 0 ; small values mean that H 0 is not plausible.

22. Copyright © 2014, 2011 Pearson Education, Inc. 22 16.2 Testing the Proportion p–Value for SPAM Software Example  Statistically significant: data contradict the null hypothesis and lead us to reject H 0 (p-value < α).  The p-value in the SPAM example is less than the typical α of 0.05; should buy the software.

23. Copyright © 2014, 2011 Pearson Education, Inc. 23 16.2 Testing the Proportion Type II Error  Power: probability that a test can reject H 0.  If a test has little power when H 0 is false, it is likely to miss meaningful deviations from the null hypothesis and produce a Type II error.

24. Copyright © 2014, 2011 Pearson Education, Inc. 24 16.2 Testing the Proportion Type II Error Probability of a Type II error if p = 0.15.

25. Copyright © 2014, 2011 Pearson Education, Inc. 25 16.2 Testing the Proportion Summary

26. Copyright © 2014, 2011 Pearson Education, Inc. 26 16.2 Testing the Proportion Checklist  SRS condition: the sample is a simple random sample from the relevant population.  Sample size condition (for proportion): both np 0 and n(1 - p 0 ) are larger than 10.

27. Copyright © 2014, 2011 Pearson Education, Inc. 27 4M Example 16.1: DO ENOUGH HOUSEHOLDS WATCH? Motivation The Burger King ad featuring Coq Roq won critical acclaim. In a sample of 2,500 homes, MediaCheck found that only 6% saw the ad. An ad must be viewed by 5% or more of households to be effective. Based on these sample results, should the local sponsor run this ad?

28. Copyright © 2014, 2011 Pearson Education, Inc. 28 4M Example 16.1: DO ENOUGH HOUSEHOLDS WATCH? Method Set up the null and alternative hypotheses. H 0 : p ≤ 0.05 H a : p > 0.05 Use α = 0.05. Note that p is the population proportion who watch this ad. Both SRS and sample size conditions are met.

29. Copyright © 2014, 2011 Pearson Education, Inc. 29 4M Example 16.1: DO ENOUGH HOUSEHOLDS WATCH? Mechanics Perform a one-sided z-test for a proportion. z = 2.3 with p-value of 0.011 Reject H 0.

30. Copyright © 2014, 2011 Pearson Education, Inc. 30 4M Example 16.1: DO ENOUGH HOUSEHOLDS WATCH? Message The results are statistically significant. We can conclude that more than 5% of households watch this ad. The Burger King Coq Roq ad is cost effective and should be run.

31. Copyright © 2014, 2011 Pearson Education, Inc. 31 16.3 Testing the Mean Similar to Tests of Proportions  The hypothesis test of µ replaces with.  Unlike the test of proportions, σ is not specified. Use s from the sample as an estimate of σ to calculate the estimated standard error of.

32. Copyright © 2014, 2011 Pearson Education, Inc. 32 16.3 Testing the Mean Example: San Francisco Rental Properties A firm is considering expanding into an expensive area in downtown San Francisco. In order to cover costs, the firm needs rents in this area to average more than $1,500 per month. Are rents in San Francisco high enough to justify the expansion?

33. Copyright © 2014, 2011 Pearson Education, Inc. 33 16.3 Testing the Mean Null and Alternative Hypotheses  Let µ = mean monthly rent for all rental properties in the San Francisco area  Set up hypotheses as: H 0 : µ ≤ µ 0 = $1,500 H a : µ > µ 0 = $1,500

34. Copyright © 2014, 2011 Pearson Education, Inc. 34 16.3 Testing the Mean t - Statistic  Used is the t-test for µ (since s estimates σ)  The t-statistic, with n-1 df, is

35. Copyright © 2014, 2011 Pearson Education, Inc. 35 16.3 Testing the Mean Example: San Francisco Rental Properties  Rents obtained for a sample of size n=115; the average rent was $1,657 with s = $581.

36. Copyright © 2014, 2011 Pearson Education, Inc. 36 16.3 Testing the Mean Example: San Francisco Rental Properties  Computing the t-statistic: t = 2.898 with 114 df; p-value = 0.0023 Reject H 0 ; mean rent exceeds break-even value.

37. Copyright © 2014, 2011 Pearson Education, Inc. 37 16.3 Testing the Mean Finding the p-Value in the t-Table Use df = 100 (closest to 114 without going over) t = 2.898 falls between 2.626 and 3.174

38. Copyright © 2014, 2011 Pearson Education, Inc. 38 16.3 Testing the Mean Summary

39. Copyright © 2014, 2011 Pearson Education, Inc. 39 16.3 Testing the Mean Checklist  SRS condition: the sample is a simple random sample from the relevant population.  Sample size condition. Unless it is known that the population is normally distributed, a normal model can be used to approximate the sampling distribution of if n is larger than 10 times the absolute value of kurtosis,.

40. Copyright © 2014, 2011 Pearson Education, Inc. 40 4M Example 16.2: COMPARING RETURNS ON INVESTMENTS Motivation Does stock in IBM return more, on average, than T-Bills? From 1990 through 2011, T- Bills returned 0.3% each month.

41. Copyright © 2014, 2011 Pearson Education, Inc. 41 4M Example 16.2: COMPARING RETURNS ON INVESTMENTS Method Let µ = mean of all future monthly returns for IBM stock. Set up the hypotheses as H 0 : µ ≤ 0.003 H a : µ > 0.003 Sample consists of monthly returns on IBM for 264 months (January 1990 – December 2011)

42. Copyright © 2014, 2011 Pearson Education, Inc. 42 4M Example 16.2: COMPARING RETURNS ON INVESTMENTS Mechanics Sample yields = 0.0126 with s = 0.0827. t = 1.886 with 263 df; p-value = 0.0302

43. Copyright © 2014, 2011 Pearson Education, Inc. 43 4M Example 16.2: COMPARING RETURNS ON INVESTMENTS Message Monthly IBM returns from 1990 through 2011 earned statistically significantly higher gains than comparable investments in U.S. Treasury Bills during this period (about 1.3% versus 0.3%).

44. Copyright © 2014, 2011 Pearson Education, Inc. 44 16.4 Significance vs Importance  Statistical significance does not mean that you have made an important or meaningful discovery.  The size of the sample affects the p-value of a test. With enough data, a trivial difference from H 0 leads to a statistically significant outcome.

45. Copyright © 2014, 2011 Pearson Education, Inc. 45 16.5 Confidence Interval or Test?  A confidence interval provides a range of parameter values that are compatible with the observed data.  A test provides a precise analysis of a specific hypothesized value for a parameter.  Most people understand the implications of confidence intervals more readily than tests.

46. Copyright © 2014, 2011 Pearson Education, Inc. 46 Best Practices  Pick the hypotheses before looking at the data.  Choose the null hypothesis on the basis of profitability.  Pick the α-level first, taking into account both types of error.  Think about whether α = 0.05 is appropriate for each test.

47. Copyright © 2014, 2011 Pearson Education, Inc. 47 Best Practices (Continued)  Make sure to have an SRS from the right population.  Use a one-sided test.  Report a p–value to summarize the outcome of a test.

48. Copyright © 2014, 2011 Pearson Education, Inc. 48 Pitfalls  Do not confuse statistical significance with substantive importance.  Do not think that the p–value is the probability that the null hypothesis is true.  Avoid cluttering a test summary with jargon.