Chapter 24. Comparing Means

Transcription

1 Chapter 4 Comparing Means!1 /34

2 Homework p579, 5, 7, 8, 10, 11, 17, 31, 3! /34

3 !3 /34

4 Objective Students test null and alternate hypothesis about two!4 /34

5 Plot the Data The intuitive display for comparing two groups is box-and-whisker plots of the data for the two groups, arranged for easy comparison. For example: Of course, the means of the two groups are not part of the display but the comparison will give you a heads up when comparing means. What do you think are the values of the means for the two groups?!5 /34

6 Comparing Two Means You have just spent time comparing proportions for two groups. Comparing two means is very similar to comparing two proportions. The parameter in these cases is the difference between the two means; Remember, for independent random values, variances add. The standard deviation of the distribution of differences between two sample means is SD(y y ) = σ 1 + σ 1 n When we do not know the true standard deviations of the two groups we use the standard error SE(y y ) = s 1 + s 1 n!6 /34

7 Comparing Two Means When working with means and the standard error of the difference between means, the appropriate test statistic for the sampling model is Student s t. We build a confidence interval called a two-sample t-interval for the difference between sample means. The corresponding hypothesis test is called a two-sample t-test.!7 /34

8 TI-84 Inpt: Data Stats Inpt: Data Stats x1: List1: Two sample t - interval. STAT TESTS 0:-SampleTInterval Sx1: n1: x: Sx: n: C-Level: Pooled: No Yes Calculate List: Freq1: Freq: C-Level: Pooled: No Yes Calculate Two sample t - test. Inpt: Data Stats Inpt: Data Stats x1: List1: STAT TESTS 4:-SampleTTest Sx1: n1: x: Sx: n: µ1: µ0 <µ0 >µ0 Pooled: No Yes Calculate List: Freq1: Freq: µ1: µ0 <µ0 >µ0 Pooled: No Yes Calculate!8 /34

9 Comparing Two Means When the conditions are met, the standardized sample difference between the means of two independent groups is t = (y 1 y ) (µ 1 µ ) s 1 + s n modeled by a Student s t with a number of degrees of freedom found with a special formula. We estimate the standard error with SE(y y ) = s 1 + s 1 n!9 /34

10 Assumptions and Conditions Independence Assumption (Each condition needs to be checked for both groups.): Randomization Condition: Were the data collected with suitable randomization (representative random samples or a randomized experiment)? 10% Condition: This condition is rarely applied for differences of means. Check it for means only with very small populations or an extremely large sample.!10 /34

11 Assumptions and Conditions Normal Population Assumption: Sufficiently Normal Condition: must be checked for both groups. A violation by either one violates the condition. Check by examining a histogram for both samples. Independent Groups Assumption: The two groups we are comparing must be independent of each other. For dependent groups there is a special test that will be covered in the next chapter.!11 /34

12 Two-Sample t-interval With appropriate conditions met, we find the confidence interval for the difference between means of two independent groups, μ 1 μ. (y 1 y ) ± t * s 1 + s n SE(y y ) = s 1 + s 1 n The critical value t* (or t df ) depends on the particular confidence level, C, that you specify and on the number of degrees of freedom, which we get from the sample sizes and a special formula..!1 /34

13 Degrees of Freedom The special formula for the degrees of freedom for our t critical value ain t fun: df = 1 1 s 1 + s n 1 s 1 n + 1 n 1 s n Because of this, we will let technology calculate degrees of freedom for us when we find a -SampleTInterval and -SampleTTest.!13 /34

14 Testing the Difference The hypothesis test we use is the two-sample t-test for means. The conditions for a two-sample t-test for the difference between means of two independent groups are the same as for the two-sample t-interval. We test the hypothesis H 0 : μ 1 μ = Δ 0, where the hypothesized difference, Δ 0, is nearly always 0, using the statistic t = (y 1 y ) (µ 1 µ ) s 1 + s N 0, n s 1 + s n!14 /34

15 Example A study was conducted to compare college majors requiring 1 or more hours of mathematics to majors that required less tha hours of math. The mean income of 8 randomly chosen majors requiring more tha hours of math was $45,69/year, with a standard deviation of $136. The mean income of 10 randomly chosen majors that require less tha hours of math was $40,416, with a standard deviation of $534. Test for a difference in incomes at α =.01. What sampling method was used? We will test the difference between the sample means and compare to a hypothesized population difference of 0. H0: µ1 = µ and H1: µ1 µ or H0: μ1 μ = 0 and H1: μ1 μ 0 µ1 = mean requiring 1 or more hours µ = mean requiring less tha hours!15 /34

16 Example 8 majors more tha hours x = $45,69/year, s = $136 α = majors less tha hours x = $40,416, s = $534 Independence Assumption: Independence Condition: The individual majors are independent. Randomization Condition: The majors were randomly selected. 10% Condition: 8 and 10 are less tha0% of majors offered. Normal Population Assumption: Nearly Normal Condition: Our samples are small but it is reasonable to assume salaries are normally distributed for all majors. Independent Groups Assumption: The two groups are independent.!16 /34

17 Example 8 majors more tha hours x = $45,69/year, s = $136 α = majors less tha hours x = $40,416, s = $534 We will conduct a -tailed -Sample T-test for the model N(0, ) STAT TESTS 4:-SampleTTest Result Inpt: Data Stats x1: 4569 Sx1: 136 n1: 8 x: Sx: 534 n: 10 µ1: µ0 <µ0 >µ0 Pooled: No Yes Calculate µ1 µ t= p= df= x1= 4569 x: Sx1: 136 Sx: 534 n1= 8 n: 10!17 /34

18 Draw a picture The t score we calculated, does not reach the rejection region. The p-value is greater than.005. Result µ1 µ t= p= df= t = t* = x1= 4569 x: Sx1: 136 Sx: 534 n1= 8 n: 10 Fail to reject H0.!18 /34

19 Example Remember to calculate the test statistic. No need to calculate df. ( ) ( ) µ µ 1 t = X 1 X s 1 + s n = ( ) = df = 1 n 1 1 s 1 s 1 + s n + 1 n 1 s n = df = 10.19!19 /34

20 Example Here is where df comes into play: Result µ1 µ nd Vars - 4:invT(.995, ) t= p= t* = df= x1= 4569 p(t > 3.033) = p(x1 - x > 576) =.013 x: Sx1: 136 t = < Fail to reject the null.01 >.005 Sx: 534 n1= 8 n: 10 There is not sufficient evidence at the.01 level to suggest that the mean income for majors requiring 1 hours of math is different than the mean income for majors requiring less tha hours of math.!0 /34

21 When to pool Remember with the tests for proportion, we know its standard deviation. So, testing the null hypothesis that two proportions are equal, the variances must be equal as well. That allowed us to pool the data for the hypothesis test. For means, there is also a pooled t-test. Like the two-proportions z-test, this test is used when we assume that the variances in the two groups are equal. But, we must be careful, as there is no direct connection between a mean and the value of the standard deviation If we are willing to assume that the variances of two means are equal, we can pool the data from two groups to improve our estimate of the population variance and make the degrees of freedom formula much simpler.!1 /34

22 *The Pooled t-test We are still estimating the pooled standard deviation from the data, so we use Student s t-model, and the test is called a pooled t-test for the difference between means. If we assume that the variances are equal, we can estimate the common variance from the numbers we already have: s pooled = ( 1)s + (n 1)s 1 n 1 1 ( ) ( ) + n 1 SE pooled (y 1 y ) = s pooled n Our degrees of freedom becomes df = + n.! /34

23 *The Pooled t-test The conditions for the pooled t-test and corresponding confidence interval are the same as for our earlier two-sample t procedures, with the additional assumption that the variances of the two groups are the same. For a hypothesis test, the calculated test statistic is: t = (y 1 y ) (µ 0 µ 0 ) df = + n s pooled n The confidence interval would be: (y 1 y ) ± t df * s pooled n!3 /34

24 To Pool or Not to Pool So, now that I have told you that, when should you use pooled-t methods rather than two-sample t methods? Never. (OK, rarely.) Because the advantages of pooling are small, and you are allowed to pool only rarely (when the equal variance assumption is met), we do not pool. It s never wrong not to pool. Not pooling is a more conservative test or interval. Wider interval, more difficult to reject the null hypothesis. So let us make it simple. When comparing means, do not pool.!4 /34

25 Variances Are Equal? There is a hypothesis test that will test for homogeneity of variance. The homogeneity of variance test is very sensitive to failures of the necessary assumptions and is not reliable for small sample sizes. Since those are the conditions for which we need the two sample t-test there is not much justification for pooling. So, the test is unreliable under the conditions most effected by pooling and when we would most like to pool.!5 /34

26 Never? Objective Students test null and Is there a place for pooling variances from two groups? Yeperdoo. In a randomized comparative experiment, we begin by randomly assigning experimental units (experimental units!) to treatments. So each treatment group comes from the same population and thus begins with the same population variance. In this case, assuming the variances are equal is a more plausible assumption, but there are conditions that still need to be verified to justify the assumption of equal variance. We ain t goin there.!6 /34

27 Caution Do not assume that because you have two samples you then have two independent samples. We have not yet talked about paired data (matched pairs). That will be a new (much simpler) test. Make certain your groups are independent. The difference between two means, and the mean of differences are most assuredly not the same. Know which you have. Make certain your groups are independent when you use the two sample t test.!7 /34

28 Example Resting pulse rates for a random sample of 6 smokers had a mean of 80 beats per minute (bpm) and a standard deviation of 5 bpm. Among 3 randomly chosen nonsmokers, the mean and standard deviation were 74 and 6 bpm. Both sets of data were roughly symmetric and had no outliers. Is there evidence of a difference in mean pulse rate between smokers and nonsmokers? How big? We will test the difference between the sample means and compare to a hypothesized difference of 0. H0: μsmokers = μnon-smokers H1: μsmokers μnon-smokers or H 0: μsmokers μnon-smokers = 0 H1: μsmokers μnon-smokers 0!8 /34

29 Example 6 smokers: mean of 80 bpm, standard deviation 5 bpm. α =.01 3 nonsmokers: mean and standard deviation were 74 and 6 bpm. Independence Assumption: Independence Condition: The individuals are independent. Randomization Condition: Samples were randomly selected. 10% Condition: 6 and 3 are less tha0% of population of smokers and non-smokers. Normal Population Assumption: Nearly Normal Condition: We are told the populations are normal. Independent Groups Assumption: The two groups are certainly independent.!9 /34

30 Example 6 smokers: mean of 80 bpm, standard deviation 5 bpm. α =.05 3 nonsmokers: mean and standard deviation were 74 and 6 bpm. We will conduct a -tailed -Sample T-test for the model N(0, ). t* = nd Vars - 4:invT(.975, ) =.0033 Stat - Tests - 4:-SampTTest Result Inpt: Data Stats x1: 80 Sx1: 5 n1: 6 x: 74 Sx: 6 n: 3 µ1: µ0 <µ0 >µ0 Pooled: No Yes Calculate *Note the p-value* *Note the df* µ1 µ t= p= x10-4 df= x1: 80 x: 74 Sx1: 5 Sx: 6 n1: 6 n: 3!30 /34

31 Example t = t* = Result µ1 µ t= p= x10-4 df= p(t < or t > ) = p(x1 - x > 576) x1: 80 x: 74 Sx1: 5 Sx: 6 =.0001 n1: 6 n: 3 Decision: Because the P-value is so small, the observed difference is unlikely to be just sampling error. We reject the null hypothesis. Conclusion: We have strong evidence of a difference in mean resting pulse rates for smokers and nonsmokers. Based on the data it is plausible that the mean pulse rate for non-smokers is lower that the mean pulse rate for smokers.!31 /34

32 Example Calculate t, do not calculate df. Let the calculator handle that. ( ) ( ) µ µ 1 t = X 1 X s 1 + s n = ( ) = df = 1 n 1 1 s 1 s 1 + s n + 1 n 1 s n = df = !3 /34

33 Example What is the difference in mean pulse rate between smokers and non-smokers? Since we rejected the null hypothesis that the difference is 0, the next step is to find an interval within which we believe the true difference falls. We will find a 95% confidence t-interval for the difference between mean pulse rates mean with model N(6, ) The assumptions have been met in the previous hypothesis test. t* = nd Vars - 4:invT(.975, ) =.0033 (y 1 y ) ± t * s 1 + s n 5 = (80 74) ± = 6 ±.8937 (3.1063, )!33 /34

34 Example (3.1063, ) Stat - Tests - 0:-SampTInt(Stats, 80, 5, 6, 74, 6, 3,.95, No) Result (3.1063, ) df= x1= 80 x: 74 We can be 95% confident that the average pulse rate for smokers is between 3.1 and 8.9 beats per minute higher than for non-smokers. Sx 1: 5 Sx : 6 n1= 6 n: 3 Interpret 95% confidence: If we were to repeatedly sample 6 smokers and 3 non-smokers, 95% of calculated intervals would capture the true difference in mean pulse rate.!34 /34