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M20_DEVE8422_02_SE_C20.indd Page 563 30/07/14 7:06 PM f-w-147 /206/PHC00112/9780321828422_DEVEAUX/DEVEAUX_DATA_AND_MODELS2ce_SE_9780321828422/S .. chapter 20 Inferences About Means 20.1 The Central Limit Theorem Revisited 20.2 Gosset’s t 20.3 A t-Interval for the Mean 20.4 Hypothesis Test for the A curve has been found representing the frequency distribution of values of the means of such samples (from a normal population), when these values are measured from the mean of the population in terms of the standard deviation of the sample . . . —William Gosset Mean 20.5 Determining the Sample T Size *20.6 The Sign Test Where are we going? We’ve learned how to generalize from the data at hand to the world at large for proportions. But not all data are as simple as “yes” or “no.” In this chapter, we’ll learn how to make confidence intervals and test hypotheses for the mean of a quantitative variable. How 30 8 15 25 15 25 20 13 Secondary school students Time to travel to school Minutes 2007–2008 Ontario To learn about the time spent by Ontario students travelling to school Taking a random sample from the Census At School data base 10 7 10 30 18 5 15 20 Time 8 15 25 10 25 2 47 5 30 10 22 25 15 5 20 15 5 35 20 8 10 25 20 12 Figure 20.1 The travel times (to school) of Ontario secondary students appear to be unimodal and perhaps slightly right-skewed. # of Students Who What Units When Where Why ravelling back and forth to work or school can be a real pain (though a good seat on the bus or subway can provide a chance to read, study, maybe snooze . . .). Since 2000, the International CensusAtSchool Project has surveyed over a million school students from Canada, the U.K., Australia, New Zealand, and South Africa using educational activities conducted in class. School participation is on a voluntary basis. Over 30 000 Canadian students participated in 2007–08. One question commonly asked in the survey is, “How long does it usually take you to travel to school?” So just how long does it take Ontario students to get to school? Times vary from student to student, but knowing the average would be helpful. As we’ve learned, a single number or estimate that is almost surely wrong is not as useful as a range of values (or confidence interval) that is almost surely correct. Using the random data selector from the CensusAtSchool project, the responses (in minutes) were obtained for a random sample of 40 participating Ontario secondary school students from 2007–2008.1 These data differ from data on proportions in one important way. Proportions are summaries of individual responses, which have two possible values, such as “yes” and “no,” “male” and “female,” or “1” and “0.” Quantitative data, however, usually report a quantitative value for each individual. Now, recall the three rules of data analysis and plot the data, as we have done here. With quantitative data, we summarize with measures of centre and spread, such as the mean and standard deviation. Because we want to make inferences, we’ll need to think about sampling distributions, which will lead us to a new sampling model. But first, some review. 9 8 7 6 5 4 3 2 1 0 0 10 20 30 Time (minutes) 40 1 www.censusatschool.ca. 563 M20_DEVE8422_02_SE_C20.indd Page 564 05/08/14 6:51 PM f-w-147 564 /206/PHC00112/9780321828422_DEVEAUX/DEVEAUX_DATA_AND_MODELS2ce_SE_9780321828422/S .. PART VI Learning About the World 20.1 The Central Limit Theorem Revisited You’ve learned how to create confidence intervals and test hypotheses about proportions. We always centre confidence intervals at our best guess of the unknown parameter. Then, we add and subtract a margin of error. For proportions, that means pn { ME. We found the margin of error as the product of the standard error, SE(pn ), and a critical value, z*, from the Normal table. So we had pn { z*SE(pn ). We knew we could use the Normal distribution because the Central Limit Theorem (CLT) told us (in Chapter 15) that the sampling distribution model for proportions is Normal. Now we want to do exactly the same thing for means, and the Central Limit Theorem (still in Chapter 15) told us that the same Normal model works as the sampling distribution for means. Here again is this fundamental theorem: THE CENTRAL LIMIT THEOREM When a random sample is drawn from any population with mean m and standard deviation s, its sample mean, y, has a sampling distribution with the same mean m s s (and we write s(y) or SD(y) = ). but whose standard deviation is 1n 1n No matter what population the random sample comes from, the shape of the sampling distribution is approximately Normal as long as the sample size is large enough. The larger the sample used, the more closely the Normal approximates the sampling distribution of the sample mean. For Example USING THE CLT (AS IF WE KNEW S) Based on weighing thousands of animals, the American Angus Association reports that mature Angus cows have a mean weight of 1309 pounds (1 pound = 0.4536 kg) with a standard deviation of 157 pounds. This result was based on a very large sample of animals from many herds over a period of 15 years, so let’s assume that these summaries are the population parameters and that the distribution of the weights was unimodal and not very severely skewed. QUESTION: What does the CLT predict about the mean weight seen in random samples of 100 mature Angus cows? ANSWER: It’s given that weights of all mature Angus cows have m = 1309 and s = 157 pounds. Because n = 100 animals is a fairly large sample, I can apply the Central Limit Theorem. I expect the resulting sample means y will average 1309 pounds and s 157 = = 15.7 pounds. have a standard deviation of SD(y ) = 1n 1100 The CLT also says that the distribution of sample means follows a Normal model, so the 68–95–99.7 Rule applies. I’d expect that ■ ■ ■ in 68% of random samples of 100 mature Angus cows, the mean weight will be between 1309 −15.7 = 1293.3 and 1309 + 15.7 = 1324.7 pounds; in 95% of such samples, 1277.6 … y … 1340.4 pounds; in 99.7% of such samples, 1261.9 … y … 1356.1 pounds. The CLT says that all we need to model the sampling distribution of y is a random sample of quantitative data. And the true population standard deviation, s. Uh oh. That could be a problem. How are we supposed to know s? With proportions, we had a link between the proportion value and the standard deviation of the sample pq proportion: SD(pn ) = . And there was an obvious way to estimate the standard 7n deviation from the data: SE(pn ) = pn qn s . But for means, SD(y) = , so knowing y An 1n M20_DEVE8422_02_SE_C20.indd Page 565 05/08/14 6:51 PM f-w-147 /206/PHC00112/9780321828422_DEVEAUX/DEVEAUX_DATA_AND_MODELS2ce_SE_9780321828422/S .. CHAPTER 20 Inferences About Means STANDARD ERROR Because we estimate the standard deviation of the sampling distribution model from the data, we’ll call it a standard error. So we use the SE(y ) notation. Remember, though, that it’s just the estimated standard deviation of the sampling distribution model for means. ■ NOTATION ALERT Ever since Gosset, t has been reserved in Statistics for his distribution. A S Activity: Estimating the Standard Error. What’s the average age at which people have heart attacks? A confidence interval gives a good answer, but we must estimate the standard deviation from the data to construct the interval. 565 doesn’t tell us anything about SD(y). We know n, the sample size, but the population standard deviation, s, could be anything. So what should we do? We do what any sensible person would do: We estimate the population parameter s with s, the sample standard s deviation based on the data. The resulting standard error is SE(y) = . 1n A century ago, people used this standard error with the Normal model, assuming it would work. And for large sample sizes it did work pretty well (as mentioned earlier in optional section 16.6). But they began to notice problems with smaller samples. The sample standard deviation, s, like any other statistic, varies from sample to sample. And this extra variation in the standard error was messing up the P-values and margins of error. William S. Gosset is the man who first investigated this problem. He realized that not only do we need to allow for the extra variation with larger margins of error and P-values, but we even need a new sampling distribution model. In fact, we need a whole family of models, depending on the sample size, n. These models are unimodal, symmetric, bell-shaped models, but the smaller our sample, the more we must stretch out the tails. Gosset’s work transformed Statistics, but most people who use his work don’t even know his name. 20.2 Gosset’s t International Statistical Institute (ISI) To find the sampling distribution of y - m , Gosset simulated it by hand. s> 1n He drew 750 samples of size 4 by shuffling 3000 cards on which he’d written the heights of some prisoners and computed the means and standard deviations with a mechanically cranked calculator. (He knew m because he was simulating and knew the population from which his samples were drawn.) Today, you could repeat in seconds on a computer the experiment that took him over a year. Gosset’s work was so meticulous that not only did he get the shape of the new histogram approximately right, but he even figured out the exact formula for it from his sample. The formula was not confirmed mathematically until years later by Sir R. A. Fisher. Gosset had a job that made him the envy of many. He was the chief experimental brewer for the Guinness Brewery in Dublin, Ireland. The brewery was a pioneer in scientific brewing and Gosset’s job was to meet the demands of the brewery’s many discerning customers by developing the best stout (a thick, dark beer) possible. Gosset’s experiments often required as much as a day to make the necessary chemical measurements or a full year to grow a new crop of hops. For these reasons, not to mention his health, his sample sizes were small—often as small as three or four. When he calculated means of these small samples, Gosset wanted to compare them to a target mean to judge the quality of the batch. To do so, he followed common statistical practice of the day, which was to calculate z-scores and compare them to the Normal model. But Gosset noticed that with samples of this size, his tests weren’t quite right. He knew this because when the batches that he rejected were sent back to the laboratory for more extensive testing, too often they turned out to be OK. In fact, about three times more often than he expected. Gosset knew something was wrong, and it bugged him. Guinness granted Gosset time off to earn a graduate degree in the emerging field of Statistics, and naturally he chose this problem to work on. He figured out that when he used s the standard error, , as an estimate of the standard deviation of the mean, the shape of 1n the sampling model changed. He even figured out what the new model should be. The Guinness Company may have been ahead of its time in using statistical methods to manage quality, but they also had a policy that forbade their employees to publish. Gosset pleaded that his results were of no specific value to brewers and was allowed to publish under the pseudonym “Student,” chosen for him by Guinness’s managing director. Accounts differ about whether the use of a pseudonym was to avoid ill feelings within the company or to hide from competing brewers the fact that Guinness was using statistical methods. In fact, Guinness was alone in applying Gosset’s results in their quality assurance operations. It was a number of years before the true value of “Student’s” results was recognized. By then, statisticians knew Gosset well, as he continued to contribute to the young field of Statistics. But this important result is still widely known as Student’s t. Gosset’s sampling distribution model is always bell-shaped, but the details change with different sample sizes. When the sample size is very large, the model is nearly Normal, but when it’s small, the tails of the distribution are much heavier than the Normal. That means that values far from the mean are more common and that can be important for small samples (see Figure 20.2). So the Student’s t-models form a whole family of related M20_DEVE8422_02_SE_C20.indd Page 566 30/07/14 7:06 PM f-w-147 566 /206/PHC00112/9780321828422_DEVEAUX/DEVEAUX_DATA_AND_MODELS2ce_SE_9780321828422/S .. PART VI Learning About the World distributions that depend on a parameter known as degrees of freedom. The degrees of freedom of a distribution represent the number of independent quantities that are left after we’ve estimated the parameters. Here it’s simply the number of data values, n, minus the number of estimated parameters. When we estimate one mean, that’s just n − 1. We often denote degrees of freedom as df and the model as tdf with the degrees of freedom as a subscript. Figure 20.2 The t-model (solid curve) on 2 degrees of freedom has fatter tails than the Normal model (dashed curve). So the 68–95–99.7 Rule doesn’t work for t-models with only a few degrees of freedom. It may not look like a big difference, but a t with 2 df is more than four times as likely to have a value greater than 2 compared to a standard Normal. –4 –2 2 0 4 What Did Gosset See? We can reproduce the simulation experiment that Gosset performed to get an idea of what he saw and how he reached some of his conclusions. Gosset drew 750 samples of size 4 from data on the heights of 3000 convicts. That population looks like this:2 Mean 166.301 cm StdDev 6.4967 cm 500 400 300 200 100 142 152 162 172 Heights (cm) 182 192 Following Gosset’s example, we drew 750 independent random samples of size 4 and found their means and standard deviations.3 As we (and Gosset) expected, the distribution of the means was even more Normal.4 150 100 50 156.25 2 162.50 168.75 Means 175.00 If you have sharp eyes, you might have noticed some gaps in the histogram. Gosset’s height data were rounded to the nearest 1/8 inch, which made for some gaps. Gosset noted that flaw in his paper. 3 In fact, Gosset shuffled 3000 cards with the numbers on them and then dealt them into 750 piles of four each. That’s not quite the same thing as drawing independent samples, but it was quite good enough for his purpose. We’ve drawn these samples in the same way. 4 Of course, we don’t know the means that Gosset actually got because we randomized using a computer and he shuffled 3000 cards, but this is one of the distributions he might have gotten, and we’re pretty sure most of the others look like this as well. M20_DEVE8422_02_SE_C20.indd Page 567 30/07/14 7:06 PM f-w-147 /206/PHC00112/9780321828422_DEVEAUX/DEVEAUX_DATA_AND_MODELS2ce_SE_9780321828422/S .. CHAPTER 20 Inferences About Means 567 y - m . We know m = 166.301 cm s> 1n because we know the population, and n = 4. The values of y and s we find for each sample. Here’s what the distribution looks like: Gosset’s concern was for the distribution of 200 150 100 50 –9.00 –5.25 –1.50 t’s 2.25 6.00 It’s easy to see that this distribution is much thinner in the middle and longer in the tails than a Normal model we saw for the means themselves. This was Gosset’s principal result. 20.3 A t-Interval for the Mean To make confidence intervals or test hypotheses for means, we need to use Gosset’s model. Which one? Well, for means, it turns out the right value for degrees of freedom is df = n - 1. ■ NOTATION ALERT A PRACTICAL SAMPLING DISTRIBUTION MODEL FOR MEANS Ever since Gosset, t has been reserved in Statistics for his distribution. When certain assumptions and conditions5 are met, the standardized sample mean, t = y - m SE(y) follows a Student’s t-model with n − 1 degrees of freedom. We estimate the standard error with s SE(y ) = 1n When Gosset corrected the model for the extra uncertainty, the margin of error got bigger, as you might have guessed. When you use Gosset’s model instead of the Normal model, your confidence intervals will be just a bit wider and your P-values just a bit larger. That’s the correction you need. By using the t-model, you’ve compensated for the extra variability in precisely the right way.6 ■ NOTATION ALERT ONE-SAMPLE t-INTERVAL FOR THE MEAN When we found critical values from a Normal model, we called them z*. When we use a Student’s t-model, we’ll denote the critical values t*. When the assumptions and conditions7 are met, we are ready to find the confidence interval for the population mean, m. The confidence interval is y { t*n - 1 * SE(y ) s . 1n The critical value t*n - 1 depends on the particular confidence level, C, that you specify and on the number of degrees of freedom, n − 1, which we get from the sample size. where the standard error of the mean SE(y ) = 5 You can probably guess what they are. We’ll see them in the next section. Gosset, as the first to recognize the consequence of using s rather than s, was also the first to give the sample standard deviation, s, a different letter than the population standard deviation s, 7 Yes, the same ones, and they’re still coming in the next section. 6 M20_DEVE8422_02_SE_C20.indd Page 568 30/07/14 7:06 PM f-w-147 568 /206/PHC00112/9780321828422_DEVEAUX/DEVEAUX_DATA_AND_MODELS2ce_SE_9780321828422/S .. PART VI Learning About the World Two tail probability One tail probability Table T Values of ta 0.20 0.10 0.10 0.05 0.05 0.025 1 2 3 4 3.078 1.886 1.638 1.533 6.314 2.920 2.353 2.132 12.706 4.303 3.182 2.776 5 6 7 8 9 1.476 1.440 1.415 1.397 1.383 2.015 1.943 1.895 1.860 1.833 2.571 2.447 2.365 2.306 2.262 10 11 12 13 14 1.372 1.363 1.356 1.350 1.345 1.812 1.796 1.782 1.771 1.761 2.228 2.201 2.179 2.160 2.145 15 16 17 18 19 1.341 1.337 1.333 1.330 1.328 1.753 1.746 1.740 1.734 1.729 2.131 2.120 2.110 2.101 2.093 df Part of Table T A S Activity: Building t-Intervals with the t-Table. Interact with an animated version of Table T. A S Activity: Student’s t in Practice. Use a statistics package to find a t-based confidence interval; that’s how it’s almost always done. Using the t-Table to Find Critical Values The Student’s t-model is different for each value of degrees of freedom. Usually we find critical values and margins of error for Student’s t-based intervals with technology. Calculators or statistics programs can give critical values for a t-model for any number of degrees of freedom and for any confidence level you choose. But you can also use tables, such as Table T at the back of this book. The tables run down the page for as many degrees of freedom as can fit. For enough degrees of freedom, the t-model gets closer and closer to the Normal, so the tables give a final row with the critical values from the Normal model and label it “ ∞ df.” These tables are only a portion of the full tables, such as the one we used for the Normal model. We could have printed a table like Table Z for every df, but that’s a lot of pages and not likely to be a bestseller. One way to shorten the book is to limit ourselves to only a few values. Although it might be nice to be able to get a critical value for a 93.4% confidence interval with 179 df, in practice we usually limit ourselves to 90%, 95%, 99%, and 99.9% and selected degrees of freedom. So, Table T fits on a single page with columns for selected confidence levels and rows for selected df’s.8 For confidence intervals, the values in the table are usually enough to cover most cases of interest. If you can’t find a row for the df you need, just be conservative and use the next smaller df in the table. For Example A ONE-SAMPLE t-INTERVAL FOR THE MEAN As degrees of freedom increase, the shape of Student’s t-model changes more and more slowly. Table T at the back of the book includes degrees of freedom between 100 and 1000 selected so that you can pin down critical values for just about any df. If your df’s aren’t listed, take the cautious approach by using the next lower df value, or use technology. In 2004, a team of researchers published a study of contaminants in farmed salmon.9 Fish from many sources were analyzed for 14 organic contaminants. The study expressed concerns about the level of contaminants found. One of those was the insecticide mirex, which has been shown to be carcinogenic and is suspected to be toxic to the liver, kidneys, and endocrine system. One farm in particular produced salmon with very high levels of mirex. After those outliers are removed, summaries for the mirex concentrations (in parts per million) in the rest of the farmed salmon are: n = 150 y = 0.0913 ppm s = 0.0495 ppm. QUESTION: What does a 95% confidence interval say about mirex? df = 150 - 1 = 149 SE(y ) = s 0.0495 = = 0.0040 1n 1150 * ≈ 1.977 (from table T, using 140 df) t149 (actually, t*149 ≈ 1.976 from technology) ANSWER: So the confidence interval for m is y { t*149 * SE(y ) 0.0913 { 1.977 (0.0040) 0.0913 { 0.0079 (0.0834, 0.0992) I’m 95% confident that the mean level of mirex concentration in farm-raised salmon is between 0.0834 and 0.0992 parts per million. Student’s t -models are all unimodal, symmetric, and bell-shaped, just like the Normal. But t-models with only a few degrees of freedom have noticeably longer tails 8 You can also find tables and interactive tools on the Internet. Ronald A. Hites, Jeffery A. Foran, David O. Carpenter, M. Coreen Hamilton, Barbara A. Knuth, and Steven J. Schwager, 2004, “Global assessment of organic contaminants in farmed salmon,” Science 303: 5655, pp. 226–229. 9 M20_DEVE8422_02_SE_C20.indd Page 569 09/08/14 5:08 PM f-445 /206/PHC00112/9780321828422_DEVEAUX/DEVEAUX_DATA_AND_MODELS2ce_SE_9780321828422/S .. CHAPTER 20 Inferences About Means z OR t? If you know s, use the standard Normal model. (That’s rare!) Whenever you use s to estimate s, use t (though for large df, the t is well approximated by the standard Normal) 569 and larger standard deviation than the Normal. (That’s what makes the margin of error bigger.) As the degrees of freedom increase, the t-models look more and more like the standard Normal. In fact, the t-model with infinite degrees of freedom is exactly the standard Normal.10 This is great news if you happen to have an infinite number of data values, but that’s not likely. However, above about 60 degrees of freedom, it’s very hard to tell the difference. Of course, in the rare situation that we know s , it would be foolish not to use that information, and if we don’t have to estimate s, we can use the Normal model. When s is known. Administrators of a hospital were concerned about the prenatal care given to mothers in their part of the city. To study this, they examined the gestation times of babies born there. They drew a sample of 25 babies born in their hospital in the previous six months. Human gestation times for healthy pregnancies are thought to be well modelled by a Normal with a mean of 280 days and a standard deviation of 14 days. The hospital administrators wanted to test the mean gestation time of their sample of babies against the known standard. For this test, they use the established value for the standard deviation, 14 days, rather than estimating the standard deviation from their sample. Because they used the model parameter value for s, they based their test on the standard Normal model rather than Student’s t. Assumptions and Conditions WHEN THE ASSUMPTIONS FAIL When you check conditions, you usually hope to make a meaningful analysis of your data. The conditions serve as disqualifiers—you keep going unless there’s a serious problem. If you find minor issues, note them and express caution about your results. If the sample is not an SRS, but you believe it’s representative of some population, limit your conclusions accordingly. If there are outliers, perform the analysis both with and without them. If the sample looks bimodal, try to analyze subgroups separately. Only when there’s major trouble— like a strongly skewed small sample or an obviously nonrepresentative sample—are you unable to proceed at all. Gosset found the t-model by simulation. Years later, Sir Ronald A. Fisher showed mathematically that Gosset was right and confirmed the assumptions needed by Gosset in discovering the t curve—that we are making repeated independent draws from a Normally distributed population. Now for our practical advice: Independence Assumption Independence Assumption: The data values should be mutually independent. There’s really no way to check independence of the data by looking at the sample, but you should think about whether the assumption is reasonable. Randomization Condition: This condition is satisfied if the data arise from a random sample or suitably randomized experiment. Randomly sampled data, especially data from a simple random sample, are ideal—almost surely independent, with well-defined target population. If the data don’t satisfy the Randomization Condition, then you should think about whether the values are likely to be independent for the variables you are concerned with and whether the sample you have is likely to be representative of the population you wish to learn about. Cluster and multistage samples, though, may have bigger SEs than suggested by our formula. In the rare case that you have a sample that is a more than 10% of the population, you may want to consider using special formulas that adjust for that. But that’s not a common concern for means. Without the correction, your SE will just err on the conservative side (be too high). This is actually a violation of the independence assumption, but a good one, since the effects are known and beneficial. Normal Population Assumption Student’s t-models won’t work for data that are badly skewed. How skewed is too skewed? Formally, we assume that the data are from a population that follows a Normal model. Practically speaking, there’s no way to be sure this is true. 10 Formally, in the limit as n goes to infinity. M20_DEVE8422_02_SE_C20.indd Page 570 09/08/14 5:08 PM f-445 PART VI Learning About the World And it’s almost certainly not true. Models are idealized; real data are, well, real. The good news, however, is that even for small samples, it’s sufficient to check the . . . Nearly Normal Condition: The data come from a distribution that is unimodal and symmetric. Check this condition by making a histogram or Normal probability plot. The importance of Normality for Student’s t depends on the sample size. Just our luck: It matters most when it’s hardest to check.11 For very small samples (n 6 15 or so), the data should follow a Normal model fairly closely. Of course, with so little data, it’s rather hard to tell. But if you do find outliers or clear skewness, don’t use these methods. For moderate sample sizes (n between 15 and about 40), the t methods will work reasonably well for mildly to moderately skewed unimodal data, but would perform badly in the presence of strong skewness or outliers. Make a histogram. When the sample size is larger than 40, the t methods are generally quite safe to use, though very severe skewness can require much larger sample sizes (in which case a better approach might be to apply a non-linear transformation, like a logarithm). Be sure to make a histogram. If you find outliers in the data, it’s always a good idea to perform the analysis twice, once with and once without the outliers, even for large samples. Outliers may well hold additional information about the data, but you may decide to give them individual attention and then summarize the rest of the data. If you find multiple modes, you may have different groups that should be analyzed and understood separately. Guinness stout may be hearty, but the t-procedure is robust! The one-sample t-test is an example of a robust statistical test. We say that it is robust with respect to the assumption of Normality, or against violations of Normality. This means that although the procedure is derived mathematically from an assumption of Normality, it can still often produce accurate results even when this assumption is violated. How well does the procedure tolerate violations of assumptions? How greatly do violations perturb the accuracy of P-values and confidence levels? These are questions about the robustness of the procedure. The bigger the violations that can be tolerated, the greater is the robustness of the procedure. Robustness for most procedures will increase with the size of the sample. The robustness of the one-sample t-procedure is described above, where we see moderate robustness for sample sizes as small as 15 and remarkable robustness for samples over size 40. Pretty impressive! And the two-sample t-procedure to be discussed in the next chapter is even more robust. The usefulness of these t-procedures would be greatly compromised were it not for their high level of robustness. Similarly, many other common statistical procedures have good robustness, increasing their utility and value. For Example CHECKING ASSUMPTIONS AND CONDITIONS FOR STUDENT’S t RECAP: Researchers purchased whole farmed salmon from 51 farms in eight regions in six countries. The histogram below shows the concentrations of the insecticide mirex in 150 farmed salmon (after removing some outliers, mentioned earlier). QUESTION: Are the assumptions and conditions for inference about the mean satisfied? 11 # of Salmon 570 /206/PHC00112/9780321828422_DEVEAUX/DEVEAUX_DATA_AND_MODELS2ce_SE_9780321828422/S .. 60 40 20 0.00 0.08 0.16 Mirex (ppm) There are formal tests of Normality, but they don’t really help. When we have a small sample—just when we really care about checking Normality—these tests have very little power. So it doesn’t make much sense to use them in deciding whether to perform a t-test. We don’t recommend that you use them. M20_DEVE8422_02_SE_C20.indd Page 571 30/07/14 7:06 PM f-w-147 /206/PHC00112/9780321828422_DEVEAUX/DEVEAUX_DATA_AND_MODELS2ce_SE_9780321828422/S .. CHAPTER 20 Inferences About Means 571 ANSWER: ■ ■ Independence/Randomization: The fish were not a random sample because no simple population existed to sample from. But they were raised in many different places, and samples were purchased independently from several sources, so they were likely to be nearly independent and to reasonably represent the population of farmed salmon worldwide. Nearly Normal Condition: The histogram of the data is unimodal. Although it may be somewhat skewed to the right, this is not a concern with a sample size of 150. It’s okay to use these data for inference about farm-raised salmon. Whew, now we know that we can actually trust that mechanical confidence interval calculation done earlier! Anyone can plug into a formula; the hard part is determining whether your procedure gives trustworthy results and answers. Just Checking Every five years, Statistics Canada conducts a census in order to compile a statistical portrait of Canada and its people. Prior to 2011, there were two forms: the short questionnaire, distributed to 80% of households, and the long questionnaire12 (short-form questions plus additional questions), slogged through by the remaining one in five households, chosen at random. For estimates resulting from the additional questions appearing only on the long form, Statistics Canada would calculate a standard error. 1. Why did Statistics Canada need to calculate a standard error for long-form information, but not for the questions that appear on both the long and short forms? 2. The standard errors are calculated after re-weighting the individual results, to correct for differences between the sample proportion who are male, aged 15–24, etc., and the known (from the long form) population proportions who are male, aged 15–24, etc., so that the resulting estimates will be more precise (so, for example, if we know that 50% of residents in a region are male, and 52% of the 20% sample are male, each male is given a slightly lower weight or multiplier than each female to “correct” for the overrepresentation of males). Hence, a simple average (for quantitative variables) or simple proportion is not used. Can Statistics Canada use the t-model for standard errors and associated confidence intervals (for quantitative variables)? If simple (unweighted) averages were used instead, could we employ the t-model? 3. The standard errors calculated by Statistics Canada are bigger for geographic areas with smaller populations and for characteristics of small sub-groups in the area examined (such as people living in poverty in a middle-income neighbourhood). Why is this so? For example, why should a confidence interval for mean family income be wider for a sparsely populated area of farms in the Prairies than for a densely populated area in an urban centre? How does the t-model formula show that this will happen? [To deal with this problem, Statistics Canada classifies estimates based on “data quality” (the size of the associated standard error relative to the estimate), warns of low-quality (high standard error) estimates, and omits low-quality estimates with excessively high standard errors, since the latter are essentially noninformative and also have the potential to compromise confidentiality, due to the small number of cases] 12 In June 2010, the minority Conservative government decided to do away with the mandatory long form and to replace it with the voluntary National Household Survey, in spite of significant opposition, citing privacy concerns. M20_DEVE8422_02_SE_C20.indd Page 572 30/07/14 7:06 PM f-w-147 572 /206/PHC00112/9780321828422_DEVEAUX/DEVEAUX_DATA_AND_MODELS2ce_SE_9780321828422/S .. PART VI Learning About the World A S Activity: Intuition for t-based Intervals. A narrated review of Student’s t. 4. Suppose that in one census tract, there were 200 Aboriginal individuals in the 20% sample, and we estimate their mean annual earnings, along with the standard error and a 95% confidence interval, using the simple t-model. In another census tract, we would like to calculate a similar confidence interval, but there were only 50 Aboriginal people in the sample. What effect would the smaller number of Aboriginals in the second tract have on the 95% confidence interval? Specifically, which values used in the formula for the margin of error would change a lot and which would change only slightly? Approximately how much wider would the confidence interval based on 50 individuals be than the one based on 200 individuals? Step-by-Step Example A ONE-SAMPLE t-INTERVAL FOR THE MEAN Let’s build a 90% confidence interval for the mean travel time to school for Ontario secondary school students. The interval that we’ll make is called the one-sample t-interval. Question: What can we say about the mean travel time to school for secondary school students in Ontario? Identify the variables and review the W ’s. Make a picture. Check the distribution shape and look for skewness, multiple modes, and outliers. REALITY CHECK The histogram centres around 15–20 minutes, and the data lie between 0 and 50 minutes. We’d expect a confidence interval to place the population mean close to 15 or 20 minutes. I want to find a 90% confidence interval for the mean travel time to school for Ontario secondary school students. I have data on the travel time of 40 students in 2007–08. Here’s a histogram of the 40 travel times. # of Students State what we want to know. Identify THINK ➨ Plan the parameter of interest. 9 8 7 6 5 4 3 2 1 0 0 Model Think about the assumptions and check the conditions. 10 20 30 Time (minutes) 40 ✓ Independence Assumption: These are independent selections from the stored data. ✓ Randomization Condition: Participation was voluntary but very broad-based, so I believe the students we randomly selected from the database should be reasonably representative of Ontario. ✓ Nearly Normal Condition: The histogram of the speeds is unimodal and slightly right-skewed, but not enough to be a concern. State the sampling distribution model for the statistic. Choose your method. The conditions are satisfied, so I will use a Student’s t-model with (n - 1) = 39 degrees of freedom and find a one-sample t-interval for the mean. M20_DEVE8422_02_SE_C20.indd Page 573 30/07/14 7:06 PM f-w-147 /206/PHC00112/9780321828422_DEVEAUX/DEVEAUX_DATA_AND_MODELS2ce_SE_9780321828422/S .. CHAPTER 20 Inferences About Means Mechanics SHOW ➨ interval. 573 Calculating from the data given at the beginning of this chapter: Construct the confidence Be sure to include the units along with the statistics. n = 40 students y = 17.00 minutes s = 9.66 minutes The standard error of y is SE(y) = The 90% critical value is t*39 = 1.685 (using software), so the margin of error is The critical value we need to make a 90% interval comes from a Student’s t table, a computer program, or a calculator. We have 40 − 1 = 39 degrees of freedom. The selected confidence level says that we want 90% of the probability to be caught in the middle, so we exclude 5% in each tail, for a total of 10%. The degrees of freedom and 5% tail probability are all we need to know to find the critical value. REALITY CHECK TELL ➨ s 9.66 = = 1.53 minutes 1n 140 ME = = = t*39 * SE(y) 1.685(1.53) 2.58 minutes The 90% confidence interval for the mean travel time is 17.0 { 2.6 minutes. The result looks plausible and in line with what we thought. Conclusion I am 90% confident that the interval from 14.4 to 19.6 minutes contains the true mean travel time to school for Ontario secondary school students. Interpret the confidence interval in the proper context. When we construct confidence intervals in this way, we expect 90% of them to cover the true mean and 10% to miss the true value. That’s what “90% confident” means. A S Activity: The Real Effect of Small Sample Size. We know that smaller sample sizes lead to wider confidence intervals, but is that just because they have fewer degrees of freedom? Here’s the part of the Student’s t table that gives the critical value we needed. (See Table T in the back of the book.) To find a critical value, locate the row of the table corresponding to the degrees of freedom and the column corresponding to the probability you want. Our 90% confidence interval leaves 5% of the values on either side, so look for a one-tail probability of 0.05 at the top of the column or 90% confidence level at the bottom. The value in the table at that intersection is the critical value we need, but unfortunately, this concise table omits 39 df. The correct value lies between 1.684 and 1.690. Either be conservative and go with the bigger value, 1.690, or use software. Using Table T to look up the critical value t* for a 90% confidence level with 39 degrees of freedom. 0.05 Probability 1.684–1.690 0.20 0.10 0.10 0.05 28 1.313 1.701 2.048 2.467 2.763 29 1.311 1.699 2.045 2.462 2.756 30 1.310 1.697 2.042 2.457 2.750 32 1.309 1.694 2.037 2.449 2.738 35 1.306 1.690 2.030 2.438 2.725 40 1.303 1.684 2.021 2.423 2.704 45 1.301 1.679 2.014 2.412 2.690 50 1.299 1.676 2.009 2.403 2.678 60 1.296 1.671 2.000 2.390 2.660 Two-tail One-tail 0.05 0.025 0.02 0.01 0.01 0.005 M20_DEVE8422_02_SE_C20.indd Page 574 30/07/14 7:06 PM f-w-147 574 /206/PHC00112/9780321828422_DEVEAUX/DEVEAUX_DATA_AND_MODELS2ce_SE_9780321828422/S .. PART VI Learning About the World Of course, you can also create the entire confidence interval with the right computer software or calculator. Make a Picture, Make a Picture, Make a Picture 50 Time 40 30 20 10 0 –2 –1 0 Normal Scores 1 2 The only reasonable way to check the Nearly Normal Condition is with graphs of the data. Make a histogram of the data and verify that its distribution is unimodal and symmetric and that it has no outliers. You should also make a Normal probability plot to see that it’s reasonably straight. You’ll be able to spot deviations from the Normal model more easily with a Normal probability plot, but it’s easier to understand the particular nature of the deviations from a histogram. If you have a computer or graphing calculator doing the work, there’s no excuse not to look at both displays as part of checking the Nearly Normal Condition. Figure 20.3 A Normal probability plot of travel times looks a bit curved but close enough to straight. SO WHAT SHOULD WE SAY? Since 90% of random samples yield an interval that captures the true mean, we should say, “I am 90% confident that the interval from 14.4 to 19.6 minutes contains the mean travel time for all Ontario secondary students.” It’s also okay to say something less formal: “I am 90% confident that the average travel time for all secondary students is between 14.4 and 19.6 minutes.” Remember: Our uncertainty is about the interval, not the true mean. The interval varies randomly. The true mean travel time is neither variable nor random—just unknown. Interpreting Confidence Intervals Confidence intervals for means offer new, tempting, and wrong interpretations. Here are some things you shouldn’t say: ■ ■ ■ ■ ■ Don’t say, “90% of all Ontario secondary students take between 14.4 and 19.6 minutes to get to school.” The confidence interval is about the mean travel time, not about the times of individual students. Don’t say, “We are 90% confident that a randomly selected student will take between 14.4 and 19.6 minutes to get to school.” This false interpretation is also about individual students rather than about the mean of their times. We are 90% confident that the mean travel time of all secondary students is between 14.4 and 19.6 minutes. Don’t say, “The mean student travel time is 17.0 minutes, 90% of the time.” That’s about means, but still wrong. It implies that the true mean varies, when in fact it is the confidence interval that would have been different had we gotten a different sample. Finally, don’t say, “90% of all samples will have mean travel times between 14.4 and 19.6 minutes.” That statement suggests that this interval somehow sets a standard for every other interval. In fact, this interval is no more (or less) likely to be correct than any other. You could say that 90% of all possible samples will produce intervals that actually do contain the true mean time. (The problem is that, because we’ll never know where the true mean time really is, we can’t know if our sample was one of those 90%.) Do say, “90% of intervals found in this way cover the true value.” Or make it more personal and say, “I am 90% confident that the true mean travel time is between 14.4 and 19.6 minutes.” 20.4 Hypothesis Test for the Mean Students and their parents are naturally concerned about how long the commute to school takes. Suppose the Ministry of Education claims that the average commute time for secondary students is no greater than 15 minutes. But you’re not so sure, particularly after collecting some data and finding a sample mean higher than 15 minutes. Maybe this is just chance variation or maybe we’ve found real evidence of excessive commute times. How can we tell the difference? This calls for a hypothesis test called the one-sample t-test for the mean. You already know enough to construct this test. The test statistic looks just like the others we’ve seen. It compares the difference between the observed statistic and a hypothesized value to the standard error of the observed statistic. We’ve seen that, for means, the appropriate probability model to use for P-values is Student’s t with n − 1 degrees of freedom. M20_DEVE8422_02_SE_C20.indd Page 575 05/08/14 6:51 PM f-w-147 /206/PHC00112/9780321828422_DEVEAUX/DEVEAUX_DATA_AND_MODELS2ce_SE_9780321828422/S .. CHAPTER 20 Inferences About Means A S Activity: A t-Test for Wind Speed. Watch the video in the preceding activity, and then use the interactive tool to test whether there’s enough wind for electricity generation at a site under investigation. 575 ONE-SAMPLE t-TEST FOR THE MEAN The assumptions and conditions for the one-sample t-test for the mean are the same as for the one-sample t-interval. We test the hypothesis H0: m = m0 using the statistic t = y - m0 SE(y) s 1n When the conditions are met and the null hypothesis is true, this statistic follows a Student’s t-model with n − 1 degrees of freedom. We use that model to obtain a P-value. If you have to make a decision, set your a-level a priori, and reject H0 if P 6 a. The standard error of y is SE(y ) = For Example A ONE-SAMPLE t-TEST FOR THE MEAN RECAP: Researchers tested 150 farm-raised salmon for organic contaminants. They found the mean concentration of the carcinogenic insecticide mirex to be 0.0913 parts per million, with standard deviation 0.0495 ppm. As a safety recommendation to recreational fishers, the Environmental Protection Agency’s (EPA) recommended “screening value” for mirex is 0.08 ppm. QUESTION: Are farmed salmon contaminated beyond the level permitted by the EPA? ANSWER: (We’ve already checked the conditions; see p. 571.) H0: m = 0.08 13 HA: m 7 0.08 These data satisfy the conditions for inference; I’ll do a one-sample t-test for the mean: n = 150, df = 149 y = 0.0913, s = 0.0495 SE(y ) = t = 0.0495 = 0.0040 1150 0.0913 - 0.08 = 2.825 0.0040 t 0 2.825 P(t149 7 2.825) = 0.0027 (from technology) Such a low P-value provides overwhelming evidence that, in farm-raised salmon, the mirex contamination level does exceed the EPA screening value. 13 The true null hypothesis is H0: m … 0.08, but we can only test one null value for m. m = 0.08 is the conservative choice, since if we can reject m = 0.08 in favour of a larger m, we can even more convincingly reject any m smaller than 0.08. Just plug in something smaller than 0.08 for m0 and you can see the t-statistic get bigger and more statistically significant. M20_DEVE8422_02_SE_C20.indd Page 576 05/08/14 9:09 PM f-w-147 576 /206/PHC00112/9780321828422_DEVEAUX/DEVEAUX_DATA_AND_MODELS2ce_SE_9780321828422/S .. PART VI Learning About the World What if, in the example above about farm-raised salmon, you had used the standard Normal distribution instead of the t distribution? You would get essentially the same P-value. This is sometimes referred to as the large sample z-test, since the Normal distribution will s s work just fine as the sampling model when you plug SE(y) = in place of SD(y) = 1n 1n in the denominator of the standardized statistic—when n is large. Only, only, only when n is large. Step-by-Step Example A ONE-SAMPLE t-TEST FOR THE MEAN The Ministry of Transportation claims that secondary students can get to their schools in 15 minutes or less, on average (okay, we confess, we made up this claim). Question: Do the data convincingly refute this claim? State what we want to know. Make THINK ➨ Plan clear what the population and parameter are. Identify the variables and review the W’s. I want to know whether the mean travel time for students exceeds the Ministry’s claim. I have a sample 40 travel times from 2007–08. H0: Mean travel time m = 15 minutes HA: Mean travel time m 7 15 minutes Hypotheses Make a picture. Check the distribution for major skewness, multiple modes, and outliers. REALITY CHECK The histogram is clustered around 10–20 minutes, so we’d be surprised to find that the true mean was much higher than that. (The fact that 15 is within the confidence interval we’ve just found confirms this suspicion.) Model Think about the assumptions and check the conditions. State the sampling distribution model. (Be sure to include the degrees of freedom.) Choose your method. Mechanics Be sure to include the units SHOW ➨ when you write down what you know from the data. The t-statistic calculation is just a standardized value, like z. We subtract the hypothesized mean and divide by the standard error. We use the null model to find the P-value. Make a picture of the t-model centred at zero. Since this is an upper-tail test, shade the region to the right of the observed t-value. # of Students The null hypothesis is that the true mean travel time is equal to the claim. Because we’re interested in whether travel times are excessive, the alternative is one-sided. 9 8 7 6 5 4 3 2 1 0 0 10 20 30 Time (minutes) 40 ✓ Independence Assumption: Discussed earlier. ✓ Randomization Condition: Discussed earlier. ✓ Nearly Normal Condition: Discussed earlier. The conditions are satisfied, so I’ll use a Student’s t-model with (n − 1) = 39 degrees of freedom to do a one-sample t-test for the mean. From the data, n = 40 students y = 17.0 minutes s = 9.66 minutes s 9.66 SE(y) = = = 1.53 minutes. 1n 140 y - m0 17.0 - 15.0 t = = = 1.31 SE(y) 1.53 (The observed mean is 1.31 standard errors above the hypothesized value.) M20_DEVE8422_02_SE_C20.indd Page 577 05/08/14 6:51 PM f-w-147 /206/PHC00112/9780321828422_DEVEAUX/DEVEAUX_DATA_AND_MODELS2ce_SE_9780321828422/S .. CHAPTER 20 Inferences About Means 577 The P-value is the probability of observing a t-value as large as 1.31 (or larger). We can find this P-value from a table, calculator, or computer program. REALITY CHECK TELL ➨ We’re not surprised that the difference isn’t very statistically significant. Conclusion Link the P-value to your decision about H0, and state your conclusion in context. 0 t 1.31 P-value = P(t39 >1.31) = 0.099 (using software) The P-value of 0.099 says that if the true mean student travel time were 15 minutes, samples of 40 students can be expected to produce a t-statistic of 1.31 or bigger 9.9% of the time. This P-value is not very small, so I won’t reject the hypothesis of a mean travel time of 15 minutes. These data do not provide enough evidence to convince me to reject the Ministry’s claim with any real conviction. For hypothesis tests, the computed t-statistic can take on any value, so the value you get is not likely to be one found in the table. The best we can do is to trap a calculated t-value between two columns. Just look across the row with the appropriate degrees of freedom to find where the t-statistic falls. The P-value will be between the two values at the heads of the columns. Report that the P-value falls between these two values. Usually that’s good enough. For Example FINDING P-VALUES FROM TABLE T RECAP: We’ve computed a one-sample t-test for the mean mirex contamination in farmed salmon, finding t = 2.825 with 149 df. In the earlier example, we found the P-value with technology. QUESTION: How can we estimate the P-value for this upper-tail test using Table T? ANSWER: I seek P(t149 7 2.825). Table T has neither a row for 149 df nor an entry that is exactly 2.825. Here’s the part of Table T where I’ll need to work; roughly the right degrees of freedom and t-values: Two-tail probability One-tail probability Values of t␣ ␣ 0 One tail t␣ 0.20 0.10 0.10 0.05 1.288 1.286 1.656 1.653 0.05 0.025 0.02 0.01 0.01 0.005 df 140 180 1.977 1.973 2.353 2.347 2.611 2.603 Since 149 df doesn’t appear in the table, I’ll be conservative and use the next lower df value that does appear. In this table, that’s 140 df. Looking across the row for 140 df, I see that the largest t-value in the table is 2.611. According to the column heading, a t-value this large or larger will occur with probability 0.005. My t-value of 2.825 is larger than this, so I know that the probability of a t-value that large must be even smaller. I can report P 6 0.005.14 If the alternative was instead HA: m ≠ 0.08, we would report p 6 2(0.005) = 0.01, since values in both tails would now support HA. 14 M20_DEVE8422_02_SE_C20.indd Page 578 30/07/14 7:06 PM f-w-147 578 /206/PHC00112/9780321828422_DEVEAUX/DEVEAUX_DATA_AND_MODELS2ce_SE_9780321828422/S .. PART VI Learning About the World Statistical Significance and Importance Recall that “statistically significant” does not mean “actually important” or “meaningful,” even though it sort of sounds that way. In this example, it does seem possible that travel times may average to a bit above 15 minutes. If so, perhaps a larger sample would show statistical significance. So, should we try for a bigger sample? The difference between 17 minutes and 15 minutes doesn’t seem very meaningful, and even if statistically significant, it would be hard to convince the government of a need to build more schools or the public to spend more money on improving transportation modes. Looking at the confidence interval, we can say with 90% confidence that the mean travel time is somewhere between 14.4 and 19.6 minutes. Even in the worst case, if the mean travel time is 19.6 minutes, would this be a bad enough situation to convince anyone to spend more money? Probably not. It’s always a good idea when we test a hypothesis to also check the confidence interval and think about the likely values for the mean. Just Checking One disadvantage of using both long and short census forms is that estimates of characteristics that are reported on the short form will not exactly match the longform estimates. Short form summary measures are computed from a complete census, so they are the “true” values—something we don’t usually have when we do inference. 5. Suppose we use long-form data to make 95% confidence intervals for the mean age of residents for each of 100 census tracts. How many of these 100 intervals should we expect will fail to include the true mean age (as determined from the complete census data)? 6. Based only on a long-form sample, we might test a null hypothesis about the mean household income in a region. Would the power of the test increase or decrease if a region returns more long forms? Intervals and Tests Confidence intervals and hypothesis tests look at the data from different perspectives. A hypothesis test starts with a proposed parameter value and asks if the data are consistent with that value. If the observed statistic is too far from the proposed parameter value, it is less plausible that the proposed value is the truth. So we reject the null hypothesis. By contrast, a confidence interval starts with the data and finds an interval of plausible values for where the parameter may lie. The 90% confidence interval for the mean school travel time was 17.0 { 2.6 minutes, or (14.4 minutes, 19.6 minutes). If someone hypothesized that the mean time was really 15 minutes, how would you feel about it? How about 25 minutes? Because the confidence interval included the time of 15.0 minutes, it certainly looks like 15 minutes might be a plausible value for the true mean school travel time. “Plausible” sounds rather like “acceptable” as a null hypothesis, and indeed this is the case. If we wanted to test the null hypothesis that the true mean is 15 minutes, and we find that 15 lies within some confidence interval, it follows that 15 minutes is a plausible null hypothesis— at some alpha level—but what alpha level? This depends on the confidence level of the confidence interval. Confidence intervals and significance tests are built from the same calculations. Here’s the connection: The confidence interval contains all possible values for the parameter that would not be rejected, as null hypotheses, in a test (after matching up test alpha level and confidence level, as discussed below). M20_DEVE8422_02_SE_C20.indd Page 579 09/08/14 5:08 PM f-445 /206/PHC00112/9780321828422_DEVEAUX/DEVEAUX_DATA_AND_MODELS2ce_SE_9780321828422/S .. CHAPTER 20 Inferences About Means 579 More precisely, a level C confidence interval contains all of the plausible null hypothesis values that would not be rejected by a two-sided hypothesis test at alpha level 1 2 C. So a 95% confidence interval matches up with a 1 2 0.95 = 0.05, or 5% significance level test for these data. Confidence intervals are naturally two-sided, so they match up exactly with two-sided hypothesis tests. When the hypothesis is one-sided, as in our example, it matches up exactly with a one-sided confidence interval (which we are not covering in this text). To relate a one-sided hypothesis test to a two-sided confidence interval, proceed as follows: Check to see if the level C confidence interval misses the null value and supports the alternative hypothesis—that is, lies entirely within the range of values of the alternative hypothesis. If so, you can reject the null hypothesis at the (1 2 C)>2 level of significance (or P 6 (1 - C)>2). If not, the test will fail to reject the null hypothesis at the (1 2 C)>2 level (or P 7 (1 - C)>2). So if we were to use our 90% confidence interval of (14.4, 19.6) to test H0: m = m0 vs HA: m 7 m0, then any value for m0 smaller than 14.4 would have to be rejected as a null hypothesis, not at the 10% level, but rather at the 5% level of significance (P 6 0.05), since (1 - 0.90)>2 = 0.05. Degrees of Freedom Don’t divide by n. Some calculators offer an alternative button for standard deviation that divides by n instead of n 2 1. Try sticking a wad of gum over the “n” button so you won’t be tempted to use it. Use n 2 1. The parameter of the t curve, its df = n 2 1, might have reminded you of the value we divide by to find the standard deviation of the data (since, in fact, it’s the same number). When we introduced that formula, we promised to later say more about why we divide by n 2 1 rather than by n. If only we knew the true population mean, m, we would use it to calculate the sample standard deviation, giving us:15 s = Σ(y - m)2 A n (Equation 20.1) But we don’t know m, so we naturally use y instead, and that causes a problem. For any sample, the data values will generally be closer to their own sample mean than to the true population mean, m. Why is that? Imagine that we take a simple random sample of 10 students who just wrote the final exam in your very large introductory Statistics course. Suppose that the mean test score (for all students) was 70. The sample mean, y, for these 10 students won’t be exactly 70. Are the 10 students’ scores closer to 70 or y? They will tend to be closer to their own average y. So, when we calculate s using Σ(y - y)2 instead of Σ(y - m)2 in Equation 20.1, our standard deviation estimate is too small. How can we fix it? The amazing mathematical fact is that we can fix it by dividing by n − 1 instead of by n. This difference is much more important when n is small than when it’s big. The t-distribution inherits this same number and we call n − 1 the degrees of freedom.16 20.5 Determining the Sample Size How large a sample do we need? The simple answer is “more.” But more data cost money, effort, and time, so how much is enough? Suppose your computer just took half an hour to download a movie you want to watch. You’re not happy. You hear about a program that claims to download movies in less than 15 minutes. You’re interested enough to spend $29.95 for it, but only if it really delivers. So you get the free evaluation copy and test it by downloading that movie 10 different times. Of course, the mean download time is not exactly 15 minutes as 15 Statistics textbooks often use equation numbers so they can talk about equations by name. We haven’t needed equation numbers yet, but we admit it’s useful here, so this is our first. 16 Here is another way to think about df. If the data are say: 4, 5, 9, the mean is 6, and the deviations are 22, 21, 13. The sum of deviations from the sample mean must equal zero, so since the first two deviations sum to 23, the last one must be 13. Only n 2 1 deviations are truly free to vary (unlike the deviations about μ, all n of which are free to vary). Dividing a sum of squared deviations by its df is generally the best way to convert such a sum to an average. M20_DEVE8422_02_SE_C20.indd Page 580 30/07/14 7:07 PM f-w-147 580 /206/PHC00112/9780321828422_DEVEAUX/DEVEAUX_DATA_AND_MODELS2ce_SE_9780321828422/S .. PART VI Learning About the World claimed. Observations vary. If the margin of error were 4 minutes, though, you’d probably be able to decide whether the software is worth the money. Doubling the sample size would require several more hours of testing and would reduce your margin of error to a bit under 3 minutes. You’ll need to decide whether that’s worth the effort. As we make plans to collect data, we should have some idea of how small a margin of error we need to be able to draw a useful conclusion. Armed with the target ME and confidence level, we can find the sample size we’ll need. Almost. s We know that for a mean, ME = t*n - 1 * SE(y) and that SE(y) = , so we can 1n determine the sample size by solving this equation for n: ME = t*n - 1 s 1n The good news is that we have an equation; the bad news is that we won’t know most of the values we need to solve it. When we thought about sample size for proportions in Chapter 16, we ran into a similar problem. There we had to guess a working value for p to compute a sample size. Here, we need to know s. We don’t know s until we get some data, but we want to calculate the sample size before collecting the data. A guess is often good enough, but if you have no idea what the standard deviation might be, or if the sample size really matters (for example, because each additional individual is very expensive to sample or experiment on), a small pilot study can provide you with a rough estimate of the standard deviation. That’s not all. Without knowing n, we don’t know the degrees of freedom and we can’t find the critical value, t*n - 1. One common approach is to use the corresponding z* value from the Normal model. If you’ve chosen a 95% confidence level, then just use 2, following the 68–95–99.7 Rule. If your estimated sample size is, say, 60 or more, it’s probably okay—z* was a good guess. If it’s smaller than that, you may want to add a step, using z* at first, finding n, and then replacing z* with the corresponding t*n - 1 and calculating the sample size once more. For Example FINDING SAMPLE SIZE A company claims its program will allow your computer to download movies quickly. We’ll test the free evaluation copy by downloading a movie several times, hoping to estimate the mean download time with a margin of error of only 4 minutes. We think the standard deviation of download times is about 5 minutes. QUESTION: How many trial downloads must we run if we want 95% confidence in our estimate with a margin of error of only 4 minutes? ANSWER: Using z* = 1.96, solve 5 1n 1.96 * 5 1n = = 2.45 4 n = (2.45)2 = 6.0025 4 = 1.96 That’s a small sample size, so I’ll use (6 − 1) = 5 degrees of freedom17 to substitute an appropriate t* value. At 95%, t*5 = 2.571. Solving the equation one more time: 5 1n 2.571 * 5 1n = ≈ 3.214 4 n = (3.214)2 ≈ 10.33 4 = 2.571 To make sure the ME is no larger, I’ll round up, which gives n = 11 runs. So, to get an ME of 4 minutes, I’ll find the downloading times for 11 movies. 17 Ordinarily we’d round the sample size up. But at this stage of the calculation, rounding down is the safer choice. Can you see why? M20_DEVE8422_02_SE_C20.indd Page 581 30/07/14 7:07 PM f-w-147 /206/PHC00112/9780321828422_DEVEAUX/DEVEAUX_DATA_AND_MODELS2ce_SE_9780321828422/S .. CHAPTER 20 Inferences About Means 581 Sample size calculations are never exact. But, it’s always a good idea to know whether the sample size is large enough to give you a good chance of being able to tell you what you want to know, before you collect any data. On the other hand, when we are testing a null hypothesis, we will be concerned with our ability to detect departures from the null that might be of considerable practical importance, so our focus shifts from the margin of error to the power of the test. Power calculations for the t-test are more complicated than those for the single proportion test (as illustrated in Chapter 17), but the basic idea is the same. Specify the difference from the null that you believe is big enough to be of practical importance, then determine the sample size (using software) that achieves the desired power (such as 0.8 or 0.9). Make sure your test is adequately powered for important alternatives, or you risk letting those big important effects (departures from the null)—just what you were looking for—slip by undiscovered. Turning up the power of a test is like turning up the power of a microscope, allowing you to see and discern even small things more clearly— in the case of tests, it allows you to see genuine effects or differences more clearly. With low power, you may end up seeing nothing clearly, so you fall back on the status quo of the null. Let’s return to the chapter example where we tested the null hypothesis of a mean mirex content in farm-raised salmon of 0.08 ppm, the recommended screening level for this contaminant. True levels slightly above 0.08 ppm might not matter all that much, but suppose that a level as high as 0.10 ppm was considered dangerously high, meriting major remedial action. We would want to ensure that our test will lead us to correctly reject the null hypothesis when such a high contamination level is actually present. Tell your software the following: ■ ■ ■ ■ ■ ■ ■ Statistical test to be used. Here we need the one-sample t-test. Alpha level of your test. Let’s choose the very common a = 0.05. The null hypothesis. In this example, it is the screening level of m = 0.08 ppm. Directionality of alternative. Let’s make it one-sided: m 7 0.08, since we only care to detect high levels of mirex. Particular alternative (effect size) considered to be of practical importance. This is the alternative (to the null) that we want to have a good chance to detect, should it be true. We decided that m = 0.10 ppm was a dangerously high level, so that is the alternative that we enter. For purposes of comparison, we’ll also consider m = 0.09 ppm. Your guess at the standard deviation of the measurements. Let’s guess that approximately s = 0.05, perhaps from some available data or pilot study, or just by making an educated guess. Why can’t we use the s from our study? Well, remember that this is usually a planning exercise, so the study hasn’t been run yet! Desired power. Let’s aim for a rather high power of 0.95. This means that 95 times in 100 when we have a situation as bad as 0.10 ppm, we will correctly reject the null hypothesis and conclude that mirex levels are too high. Okay! Ready . . . aim . . . fire (up your software). Below is some typical output: Power and Sample Size One-Sample t Test Testing mean = null (versus > null) Calculating power for mean = null + difference Alpha = 0.05 Assumed standard deviation = 0.05 Difference 0.01 0.02 Sample Size 272 70 The sample size is for each group. Target Power 0.95 0.95 Actual Power 0.950054 0.952411 M20_DEVE8422_02_SE_C20.indd Page 582 30/07/14 7:07 PM f-w-147 582 /206/PHC00112/9780321828422_DEVEAUX/DEVEAUX_DATA_AND_MODELS2ce_SE_9780321828422/S .. PART VI Learning About the World It appears that the researchers didn’t need to test 150 salmon; 70 would have sufficed, if 0.02 ppm above the screening value is where serious problems occur. But if they felt they needed to detect a lower mirex level, like 0.09 ppm (0.01 above the screening level), 272 salmon would be required for testing. Note we have demanded a rather high power of 0.95. If you reduce this target power, smaller sample sizes will result. Actually, this power calculation is quite doable without using the computer if the sample size is not too small—at least 40 or 50. Check the last two exercises at the end of this chapter if you’d like to go through the actual calculations without using software, for moderately big samples. *20.6 The Sign Test Another and perhaps more simple way to test the Ministry’s claim of 15 minutes average school travel time would be to ignore the actual travel time data and just ask each student, “Does it take longer than 15 minutes to get to school?” So rather than record the numerical times in minutes, we could just record a “yes” (or “1”) for students who take longer than 15 minutes and a “no” (or “0”) for students who take less than 15 minutes (and we’ll ignore those who say it takes them exactly 15 minutes). But what is the actual null hypothesis that could be tested from such 0–1 data? Well, 15 minutes would be some sort of centre if roughly equal numbers of students took more than 15 minutes and less than 15 minutes. Aha! That would then make 15 minutes not the mean, but rather the median travel time, and so our null hypothesis would say that the median is 15 minutes. If this null hypothesis were true, we’d expect the proportion of students who take longer than 15 minutes to be 50%. On the other hand, if the true median time were greater than 15 minutes, we’d expect to have more than 50% of students with travel times exceeding 15 minutes. What we’ve done is turn the quantitative data about travel times into a set of yes-or-no values (Bernoulli trials from Chapter 14). And we’ve turned a question about the median time into a test of a proportion (Is the proportion of students who take more than 15 minutes to get to school greater than 0.50?). We already know how to conduct a test of proportions, so this isn’t a new situation. (Can you see why we had to throw out the data points exactly equal to 15?) When we test a hypothesized median by counting the number of values above and below that value, it’s called a sign test. The sign test is a distribution-free method (or non-parametric method), so called because there are no distributional assumptions or conditions on the data. Specifically, because we are no longer working with the original quantitative data, we aren’t requiring the Nearly Normal Condition. We already know all we need for the sign test Step-by-Step: Step-by-Step Example *A SIGN TEST THINK ➨ Plan State what we want to know. I want to know whether there is evidence that the median travel time to school for secondary school students exceeds 15 minutes. Identify the parameter of interest. Here, it is the population median. I have 34 students for the test (six students with travel times of 15 minutes were omitted) and have recorded whether or not their travel times exceeded 15 minutes. Identify the variables and review the W’s. Hypotheses Write the null and alterna- tive hypotheses. There is not a great need to plot the data. Medians are resistant to the effects of skewness or outliers. H0: The median travel time to school for Ontario secondary students is 15 minutes. Equivalently, the proportion of student travel times exceeding 15 minutes is 50%: H0: p = 0.50. M20_DEVE8422_02_SE_C20.indd Page 583 30/07/14 7:07 PM f-w-147 /206/PHC00112/9780321828422_DEVEAUX/DEVEAUX_DATA_AND_MODELS2ce_SE_9780321828422/S .. CHAPTER 20 Inferences About Means 583 HA: The true proportion of students taking more than 15 minutes is more than 0.50, or p 7 0.50. Model Think about the assumptions and check the conditions. The sign test doesn’t require the Nearly Normal Condition. ✓ Independence Assumption: Previously checked. ✓ Randomization Condition: Previously checked. ✓ 10% Condition: The data are from a large number of students (so no special adjustment is needed to our SE formula). If the Success/Failure Condition fails, we can still calculate a P-value using the Binomial model for the observed count of Successes. ✓ Success/Failure Condition: Both np0 = 34(0.5) = 17 and nq0 = 34(0.5) = 17 are greater than 10, showing that I expect at least 10 successes and at least 10 failures. Hence the Normal model for proportions may be used. Choose your method. Because the conditions are satisfied, I’ll do a sign test. This is just a test of p0 = 0.5. Mechanics We use the null model to find SHOW ➨ the P-value—the probability of observing a proportion as far from the hypothesized proportion as the one we observed, or even farther. The P-value is the probability of observing a sample proportion as large as 0.529 (or larger) when the null hypothesis is true: SD(pn) = 7 0.5 * 0.5 = 0.0857 34 Of the 34 students, 18 had times over 15 minutes (six indicated exactly 15 minutes and were dropped), so the observed proportion, pn, is 0.529. 0.529 P = P(pn Ú 0.529 p = 0.50) 0.50 The probability of observing a value 0.34 standard deviations or more above the mean of a Normal model can be found by computer, calculator, or table. 0.529 - 0.5 = 0.34, so it is 0.34 0.0857 standard deviations above the hypothesized proportion. z = The P-value is P(z 7 0.34) = 0.367. Link the P-value to your deciTELL ➨ Conclusion sion, then state your conclusion in the proper context. The P-value of 0.367 is not very small, so I fail to reject the null hypothesis. There is insufficient evidence to suggest that the median travel time is greater than 15 minutes. The sign test is simpler than the t-test, and it requires fewer assumptions. We need only yes/no data. We still should check for Independence and the Randomization Condition, but we no longer need the Nearly Normal Condition. When the data satisfy all the assumptions and conditions for a t-test on the mean, we usually prefer the t-test because it is more powerful than the sign test; for the same data, the P-value from the M20_DEVE8422_02_SE_C20.indd Page 584 30/07/14 7:07 PM f-w-147 584 /206/PHC00112/9780321828422_DEVEAUX/DEVEAUX_DATA_AND_MODELS2ce_SE_9780321828422/S .. PART VI Learning About the World t-test would be smaller than the P-value from the sign test. (In fact, the P-value for the t-test here was 0.099.) That’s because the t-test uses the actual quantitative data values, which contain much more information than just knowing whether those same values are over 15. The more information we use, the more potential for statistical significance. On the other hand, the sign test works even when the data have outliers or a skewed distribution—problems that can distort the results of the t-test and reduce its power. When we have doubts whether the conditions for the t-test are satisfied, it’s a good idea to perform a sign test.18 WHAT CAN GO WRONG? The most fundamental issue you face is knowing when to use Student’s t methods. ■ Don’t confuse proportions and means. When you treat your data as categorical, counting successes and summarizing with a sample proportion, make inferences using the (usually Normal model based) methods you learned about in Chapters 16 through 19. When you treat your data as quantitative, summarizing with a sample mean, make your inferences using Student’s t methods. Student’s t methods work well when the Normality Assumption is roughly true. Naturally, many of the ways things can go wrong turn out to be different ways that the Normality Assumption can fail. It’s always a good idea to look for the most common kinds of failure. It turns out that you can even fix some of them. ■ Beware of multimodality. The Nearly Normal Condition clearly fails if a histogram of the data has two or more modes. When you see this, look for the possibility that your data come from two groups. If so, your best bet is to try to separate the data into different groups. (Use the variables to help distinguish the modes, if possible. For example, if the modes seem to be composed mostly of men in one and women in the other, split the data according to sex.) Then you could analyze each group separately. ■ Beware of severely skewed data. Make a Normal probability plot and a histogram of the data. If the data are very skewed, you might try re-expressing the variable. Re-expressing may yield a distribution that is more nearly unimodal and symmetric, more appropriate for Student’s t inference methods for means. Re-expression cannot help if the sample distribution is not unimodal. Some people may object to re-expressing the data, but unless your sample is very large, you just can’t use the methods of this chapter on data that are severely skewed. ■ Set outliers aside – respectfully. Student’s t methods are built on the mean and standard deviation, so we should beware of outliers when using them. When you make a histogram to check the Nearly Normal Condition, be sure to check for outliers as well. If you find some, consider doing the analysis twice, both with the outliers excluded and with them included in the data, to get a sense of how much they affect the results. The suggestion that you can perform an analysis with outliers removed may be controversial in some disciplines. Setting aside outliers is seen by some as “cheating.” But an analysis of data with outliers left in place is always wrong. The outliers violate the Nearly Normal Condition and also the implicit assumption of a homogeneous population, so they invalidate inference procedures. An analysis of the nonoutlying points, along with a separate discussion of the outliers, is often much more informative and can reveal important aspects of the data. How can you tell whether there are outliers in your data? The “outlier nomination rule” of boxplots can offer some guidance, but it’s just a very rough rule of thumb and not an absolute definition. The best practical definition is that a value is an outlier if removing it substantially changes your conclusions about the data. You won’t want a single value to 18 It’s probably a good idea to routinely compute both. If they agree, then the inference is clear. If they differ, it may be interesting and important to see why. M20_DEVE8422_02_SE_C20.indd Page 585 30/07/14 7:07 PM f-w-147 /206/PHC00112/9780321828422_DEVEAUX/DEVEAUX_DATA_AND_MODELS2ce_SE_9780321828422/S .. CHAPTER 20 Inferences About Means determine your understanding of the world unless you are very, very sure that it is absolutely correct and truly “belongs” to your target population. Of course, when the outliers affect your conclusion, this can lead to the uncomfortable state of not really knowing what to conclude. Such situations call for you to use your knowledge of the real world and your understanding of the data you are working with.19 Of course, Normality issues aren’t the only risks you face when doing inferences about means. Remember to Think about the usual suspects. DON’T IGNORE OUTLIERS As tempting as it is to get rid of annoying values, you can’t just throw away outliers and not discuss them. It isn’t appropriate to lop off the highest or lowest values just to improve your results. CONNECTIONS 585 ■ Watch out for bias. Measurements of all kinds can be biased. If your observations differ from the true mean in a systematic way, your confidence interval may not capture the true mean. And there is no sample size that will save you. A bathroom scale that’s five pounds off will be five pounds off even if you weigh yourself 100 times and take the average. We’ve seen several sources of bias in surveys, and measurements can be biased, too. Be sure to think about possible sources of bias in your measurements. ■ Make sure cases are independent. Student’s t methods also require the sampled values to be mutually independent. Think hard about whether there are likely violations of independence in the data collection method. If there are, be very cautious about using these methods. ■ Make sure that data are from an appropriately randomized sample. Ideally, all data that we analyze are drawn from a simple random sample or are generated by a completely randomized experimental design. When they’re not, be careful about making inferences from them. You may still compute a confidence interval or get the mechanics of the P-value right, but this might not save you from making a serious mistake in inference. For other types of random samples, more complicated SE formulas apply. Cluster sampling in particular may have a much bigger SE than given by our formula. ■ Interpret your confidence interval correctly. Many statements that sound tempting are, in fact, misinterpretations of a confidence interval for a mean. You might want to have another look at some of the common mistakes (as explained on p. xxx). Keep in mind that a confidence interval is about the mean of the population, not about the means of samples, individuals in samples, or individuals in the population. ■ Choose your alternative hypothesis based only on what you are trying to prove. Never choose a one-sided alternative after seeing which way the data are pointing, or you will incorrectly report a P-value half its true size. If you have any doubt about the nature of the alternative, go with the conservative choice of a two-sided alternative. The steps for finding a confidence interval or hypothesis test for means are just like the corresponding steps for proportions. Even the form of the calculations is similar. As the z-statistic did for proportions, the t-statistic tells us how many standard errors our sample mean is from the hypothesized mean. For means, though, we have to estimate the standard error separately. This added uncertainty changes the model for the sampling distribution from standard Normal to t. As with all of our inference methods, the randomization applied in drawing a random sample or in randomizing a comparative experiment is what generates the sampling distribution. Randomization is what makes inference in this way possible at all. The new concept of degrees of freedom connects back to the denominator of the sample standard deviation calculation, as shown earlier. There’s just no escaping histograms and Normal probability plots. The Nearly Normal Condition required to use Student’s t can be checked best by making appropriate displays of the data. When we first used histograms, we looked at their shape and, in particular, checked whether they were unimodal and symmetric, and whether they showed any outliers. Those are just the features we check for here. The Normal probability plot zeros in on the Normal model a little more precisely. 19 An important reason for you to know Statistics rather than let someone else analyze your data. M20_DEVE8422_02_SE_C20.indd Page 586 05/08/14 6:51 PM f-w-147 586 /206/PHC00112/9780321828422_DEVEAUX/DEVEAUX_DATA_AND_MODELS2ce_SE_9780321828422/S .. PART VI Learning About the World What Have We Learned? Learning Objectives Know the sampling distribution of the mean. ■ To make inferences using the sample mean, we typically will need to estimate its standard deviation. This standard error is given by: s SE(y) = . 1n ■ When we use the SE instead of the SD, the sampling distribution model that allows for the additional uncertainty is Student’s t. Construct confidence intervals for the true mean, m. ■ ■ ■ ■ A confidence interval for the mean has the form y { ME. The Margin of Error is ME = t*dfSE(y). Find t* values by technology or from tables. When constructing confidence intervals for means, the correct degrees of freedom is n 2 1. ■ Check the Assumptions and Conditions before using any sampling distribution for inference. Perform hypothesis tests for the mean using the standard error of y as a ruler and then finding the P-value from Student’s t* model on n 2 1 degrees of freedom. Write clear summaries to interpret a confidence interval or state a hypothesis test’s conclusion. Find the sample size needed to produce a given margin of error or to produce desired power in a test of hypothesis. Review of Terms Student’s t A family of distributions indexed by its degrees of freedom. The t-models are unimodal, symmetric, and bell-shaped, but generally have fatter tails and a narrower centre than the Normal model. As the degrees of freedom increase, t-distributions approach the standard Normal (p. 565). Degrees of freedom for Student’s t-distribution For the application of the t-distribution in this chapter, the degrees of freedom are equal to n 2 1, where n is the sample size (p. 566). One-sample t-interval for the mean A one-sample t-interval for the population mean is y { t*n - 1 * SE(y), where SE(y) = s 1n The critical value t*n - 1 depends on the particular confidence level, C, that you specify and on the number of degrees of freedom, n 2 1 (p. 567). One-sample t-test for the mean Sign test The one-sample t-test for the mean tests the hypothesis H0: m = m0 using the statistic y - m0 s where the standard error of y is SE(y) = (p. 574). t = SE(y) 1n A distribution-free test of a hypothesized median (p. 582). On the Computer INFERENCE FOR MEANS Statistics packages offer convenient ways to make histograms of the data. Even better for assessing near-Normality is a Normal probability plot. When you work on a computer, there is simply no excuse for skipping the step of plotting the data to check that it is nearly Normal. Beware: Statistics packages don’t agree on whether to place the Normal scores on the x-axis (as we have done) or the y-axis. Read the axis labels. M20_DEVE8422_02_SE_C20.indd Page 587 30/07/14 7:07 PM f-w-147 /206/PHC00112/9780321828422_DEVEAUX/DEVEAUX_DATA_AND_MODELS2ce_SE_9780321828422/S .. CHAPTER 20 Inferences About Means 587 Any standard statistics package can compute a hypothesis test. Here’s what the package output might look like in general (although no package we know gives the results in exactly this form):20 Null hypothesis Test Ho: (speed) = 30 vs Ha: Sample Mean = 31.043478 t = 1.178 w/22 df P-value = 0.1257 A S Activity: Student’s t in Practice. We almost always use technology to do inference with Student’s t. Here’s a chance to do that as you investigate several questions. Alternative hypothesis (speed) > 30 The P-value is usually given last The package computes the sample mean and sample standard deviation of the variable and finds the P-value from the t-distribution based on the appropriate number of degrees of freedom. All modern statistics packages report P-values. The package may also provide additional information, such as the sample mean, sample standard deviation, t-statistic value, and degrees of freedom. These are useful for interpreting the resulting P-value and telling the difference between a meaningful result and one that is merely statistically significant. Statistics packages that report the estimated standard deviation of the sampling distribution usually label it “standard error” or “SE.” Inference results are also sometimes reported in a table. You may have to read carefully to find the values you need. Often, test results and the corresponding confidence interval bounds are given together. And often you must read carefully to find the alternative hypotheses. Here’s an example of that kind of output: 0 Calculated mean, Hypothesized value Estimated mean DF Std Error Alpha 0.05 1-sided HA: >30 Statistic Prob > ⎢t ⎢ Prob > t Prob < t 30 31.043478 22 0.886 tTest 1.178 0.2513 0.1257 0.8743 t-statistic tinterval Upper 95% Lower 95% 2-sided alternative (note the ) 1-sided HA: The alpha level often defaults to 0.05. Some packages let you choose a different alpha level 32.880348 29.206608 P-values for each alternative Corresponding confidence interval <30 DATA DESK Select variables. From the Calc menu, choose Estimate for confidence intervals, or Test for hypothesis tests. Select the interval or test from the drop-down menu, and make other choices in the dialogue. 20 Power and sample size calculations are not available. Many statistics packages keep as many as 16 digits for all intermediate calculations. If we had kept as many, our results in the Step-By-Step section would have been closer to these. M20_DEVE8422_02_SE_C20.indd Page 588 05/08/14 6:51 PM f-w-147 588 /206/PHC00112/9780321828422_DEVEAUX/DEVEAUX_DATA_AND_MODELS2ce_SE_9780321828422/S .. PART VI Learning About the World EXCEL Specify formulas. Find t* with the TINV(alpha, df) function. COMMENTS Not really automatic. There’s no easy way to find P-values or to perform power and sample size calculations in Excel. JMP From the Analyze menu, select Distribution. For a confidence interval, scroll down to the “Moments” section to find the interval limits. For a hypothesis test, click the red triangle next to the variable’s name, and choose Test Mean from the menu. Then, fill in the resulting dialogue. COMMENTS “Moment” is a fancy statistical term for means, standard deviations, and other related statistics. For power and sample size calculations, proceed as follows: ■ Choose Power and Sample Size from the DOE menu. ■ Choose One Sample Mean from the submenu. ■ Indicate the Difference in the means that you are hoping to detect, the Alpha value, and choose a one-or two-sided alternative. ■ Guess at the Std Dev. ■ Fill in either your desired Sample size or Power. The one you leave blank will be calculated. ■ Click Continue. MINITAB From the Stat menu, choose the Basic Statistics submenu. From that menu, choose 1-sample t. . . . Then, fill in the dialogue. For power and sample size calculations: From the Stat menu, choose the Basic Statistics, then Power and Sample Size, then 1-Sample t. . . . In the dialogue box, fill in any two of Sample Sizes, Differences, Power values. Make your best guess at the value for the Standard deviation. And be sure to check the Options for the correct alternative hypothesis and significance level. For Difference, fill in the difference between the null value for the mean and the alternative value of the mean at which you are doing the calculation. No need to indicate the null value anywhere, as only the difference matters. R To test the hypothesis that m = mu (default is mu = 0) against an alternative (default is two-sided) and to produce a confidence interval (default is 95%), create a vector of data in x and then: ■ t.test(x, alternative = c(“two.sided” , “less”, “greater”), mu = 0, conf.level = 0.95) provides the t-statistic, P-value, degrees of freedom, and the confidence interval for a specified alternative. COMMENTS The dialogue offers a clear choice between confidence interval and test. M20_DEVE8422_02_SE_C20.indd Page 589 12/08/14 9:05 PM f-w-147 /206/PHC00112/9780321828422_DEVEAUX/DEVEAUX_DATA_AND_MODELS2ce_SE_9780321828422/S .. CHAPTER 20 Inferences About Means In package {pwr} (not installed by default), to perform a sample size or power calculation for a one-sample t-test, ■ pwr.t.test(n= , d= , sig.level= , power= , type= “one.sample”) returns one argument that is not specified in the function. For example, for fixed a = 5%, 80% power, and effect size, d, of 0.5, pwr.t.test(d=0.5, sig.level=0.05, power=0.8, type=”one.sample”) will return a sample size of 33.36713 (i.e., 34). Use the alternative= “two.sided,” “less,” or “greater” attribute to perform one-sided tests. In the case of using “less,” your effect size should be negative. 589 COMMENTS The effect size, d, required by R is equal to the difference between the alternative and null means divided by the population standard deviation. Since you often won’t have data when doing this calculation, the SD needs to be guessed. A pilot study can help. SPSS From the Analyze menu, choose the Compare Means submenu. From that, choose the One-Sample t-test command. COMMENTS The commands suggest neither a single mean nor an interval. But the results provide both a test and an interval. You need the IBM SPSS SamplePower add-on for power and sample size calculations. STATCRUNCH To do inference for a mean using summaries: ■ Click on Stat. ■ Choose T Statistics » One sample » with summary. ■ Enter the Sample mean, Sample std dev, and Sample size. ■ Click on Next. ■ ■ ■ Indicate Hypothesis Test, then eneter the hypothesized Null mean, and choose the Alternative hypothesis. OR ■ To do inference for a mean using data: ■ Click on Stat. ■ Choose T Statistics » One sample » with data. ■ Choose the variable Column. ■ Click on Next. Indicate Confidence Interval, and then enter the Level of confidence. Click on Calculate. Indicate Hypothesis Test, then enter the hypothesized Null mean, and choose the Alternative hypothesis. OR ■ Indicate Confidence Interval, then entre the Level of confidence. ■ Click on Calculate. Power & Sample size calculations are readily available, using Stat » T Statistics » One Sample » Power/Sample size. ■ Click on Hypothesis Test Power. ■ Fill in all the boxes except for the one that you want to determine, either Power or Sample Size. ■ Make a guess at the Standard deviation. TI-83/84 PLUS Finding a confidence interval: In the STAT TESTS menu, choose 8:TInterval. You may specify that you are using data stored in a list, or you may enter the mean, standard deviation, and sample size. You must also specify the desired level of confidence. Power and sample size calculations not provided. Testing a hypothesis: In the STAT TESTS menu, choose 2:T-Test. You may specify that you are using data stored in a list, or you may enter the mean, standard deviation, and size of your sample. You must also specify the hypothesized model mean and whether the test is to be two-tail, lower-tail, or upper-tail. M20_DEVE8422_02_SE_C20.indd Page 590 30/07/14 7:07 PM f-w-147 590 /206/PHC00112/9780321828422_DEVEAUX/DEVEAUX_DATA_AND_MODELS2ce_SE_9780321828422/S .. PART VI Accessing Associations Between Variables Exercises 1. t-models, part I Using the t tables, software, or a calculator, estimate a) the critical value of t for a 90% confidence interval with df = 17. b) the critical value of t for a 98% confidence interval with df = 88. c) P(t Ú 2.09 if 4 df) d) P( t 7 1.78 if 22 df) 2. t-models, part II Using the t tables, software, or a calculator, estimate a) the critical value of t for a 95% confidence interval with df = 7. b) the critical value of t for a 99% confidence interval with df = 102. c) P(t Ú 2.19 if 41 df) d) P( t 7 2.33 if 12 df) 3. t-models, part III Describe how the shape, centre, and spread of t-models change as the number of degrees of freedom increases. 4. t-models, part IV (last one!) Describe how the critical value of t for a 95% confidence interval changes as the number of degrees of freedom increases. 5. Cattle Researchers give livestock a special feed supplement to see if it will promote weight gain. They report that the 77 cows studied gained an average of 56 pounds, and that a 95% confidence interval for the mean weight gain this supplement produces has a margin of error of {11 pounds. Some students wrote the following conclusions. Did anyone interpret the interval correctly? Explain any misinterpretations. a) 95% of the cows studied gained between 45 and 67 pounds. b) We’re 95% sure that a cow fed this supplement will gain between 45 and 67 pounds. c) We’re 95% sure that the average weight gain among the cows in this study was between 45 and 67 pounds. d) The average weight gain of cows fed this supplement will be between 45 and 67 pounds 95% of the time. e) If this supplement is tested on another sample of cows, there is a 95% chance that their average weight gain will be between 45 and 67 pounds. 6. Viewing hours Software analysis of the weekly hours spent by Canadian secondary school students viewing television, videos, or movies from a random sample of 200 students produced the t-interval shown below. Which conclusion, from the choices below, is correct? What’s wrong with the others? With 90% Confidence, 8.6 6 m (weekly viewing hours) 6 10.8 a) If we took many random samples of Canadian secondary students, about 9 out of 10 of them would produce this confidence interval. b) If we took many random samples of Canadian secondary students, about 9 out of 10 of them would produce a confidence interval that contained the mean weekly television, video, or movie viewing time of all Canadian secondary students. c) About 9 out of 10 Canadian secondary students spend between 8.6 and 10.8 hours per week on television, videos, or movies. d) About 9 out of 10 of the students surveyed spend between 8.6 and 10.8 hours per week on television, video, or movie viewing. e) We are 90% confident that the average time spent viewing television, videos, or movies by secondary students in Canada is between 8.6 and 10.8 hours per week. 7. Meal plan After surveying students at Dartmouth College, a campus organization calculated that a 95% confidence interval for the mean cost of food for one term (of three in the Dartmouth trimester calendar) is ($1372, $1562). Now the organization is trying to write its report and is considering the following interpretations. Comment on each. a) 95% of all students pay between $1372 and $1562 for food. b) 95% of the sampled students paid between $1372 and $1562. c) We’re 95% sure that students in this sample averaged between $1372 and $1562 for food. d) 95% of all samples of students will have average food costs between $1372 and $1562. e) We’re 95% sure that the average amount all students pay is between $1372 and $1562. 8. Snow Based on meteorological data for the past century, a local television weather forecaster estimates that the region’s average winter snowfall is 58 cm, with a margin of error of 5 cm. Assuming he used a 95% confidence interval, how should viewers interpret this news? Comment on each of these statements (assuming a lack of systematic climate change): a) During 95 of the past 100 winters, the region got between 53 cm and 63 cm of snow. b) There’s a 95% chance that the region will get between 53 cm and 63 cm of snow this winter. c) There will be between 53 cm and 63 cm of snow on the ground for 95% of winter days. d) Residents can be 95% sure that the area’s average snowfall is between 53 cm and 63 cm. e) Residents can be 95% confident that the average snowfall during the past century was between 53 cm and 63 cm per winter. M20_DEVE8422_02_SE_C20.indd Page 591 11/08/14 6:02 PM f-447 /206/PHC00112/9780321828422_DEVEAUX/DEVEAUX_DATA_AND_MODELS2ce_SE_9780321828422/S .. CHAPTER 20 Inferences About Means 9. Pulse rates A medical researcher measured the pulse rates (beats per minute) of a sample of randomly selected adults and found the following Student’s t-based confidence interval: With 95.00% Confidence, 70.887604 6 m(Pulse) 6 74.497011 a) Explain carefully what the software output means. b) What is the margin of error for this interval? c) If the researcher had calculated a 99% confidence interval, would the margin of error be larger or smaller? Explain. A computer program found that the resulting 95% confidence interval for the mean amount spent in March 2013 is (−$28366.84, $90691.49). Explain why the analysts didn’t find the confidence interval useful, and explain what went wrong. 13. Normal temperature The researcher described in Exercise 9 also measured the body temperatures of that randomly selected group of adults. The data he collected are summarized below. We wish to estimate the average (or “normal”) temperature among the adult population. 10. Crawling Data collected by child development scientists produced this confidence interval for the average age (in weeks) at which babies begin to crawl: Summary Count 52 Mean 36.83°C Median 36.78°C MidRange 37.00°C StdDev 0.38 Range 1.55 IntQRange 0.58 t-Interval for m 29.202 6 m(age) 6 31.844 (95.00% Confidence): a) Explain carefully what the software output means. b) What is the margin of error for this interval? c) If the researcher had calculated a 90% confidence interval, would the margin of error be larger or smaller? Explain. Number of CEOs 15 10 6 4 36.0 36.6 37.2 37.8 Body Temperature (°C) 5 0 10 20 30 40 50 60 70 Total Compensation ($ Million) Based on these data, a computer program found that a 95% confidence interval for the mean annual compensation of all Forbes 500 CEOs is (1.69, 14.20) $ million. Why should you be hesitant to trust this confidence interval? 12. Credit card charges A credit card company takes a random sample of 100 cardholders to see how much they charged on their card last month. Here’s a histogram: 80 60 40 20 0 8 2 0 0 10 # of Participants 11. CEO compensation A sample of 20 CEOs from the Forbes 500 shows total annual compensations ranging from a minimum of $0.1 million to $62.24 million. The average for these 20 CEOs is $7.946 million. Here’s a histogram: Frequency T 591 500,000 1,500,000 2,500,000 March 2005 Charges a) Are the necessary conditions for a t-interval satisfied? Explain. b) Find a 98% confidence interval for mean body temperature. c) Explain the meaning of that interval. d) Explain what “98% confidence” means in this context. e) 37°C is commonly assumed to be “normal.” Do these data suggest otherwise? Explain. 14. Parking Hoping to lure more shoppers downtown, a city builds a new public parking garage in the central business district. The city plans to pay for the structure through parking fees. During a two-month period (44 weekdays), daily fees collected averaged $126, with a standard deviation of $15. a) What assumptions must you make in order to use these statistics for inference? b) Write a 90% confidence interval for the mean daily income this parking garage will generate. c) Explain in context what this confidence interval means. d) Explain what “90% confidence” means in this context. e) The consultant who advised the city on this project predicted that parking revenues would average $130 per day. Based on your confidence interval, do you think the consultant was correct? Why? M20_DEVE8422_02_SE_C20.indd Page 592 30/07/14 7:07 PM f-w-147 592 /206/PHC00112/9780321828422_DEVEAUX/DEVEAUX_DATA_AND_MODELS2ce_SE_9780321828422/S .. PART VI Accessing Associations Between Variables 15. Normal temperatures, part II Consider again the stac) According to Stigler (who reports these values), the tistics about human body temperature in Exercise 13. true speed of light is 299 710.5 km/sec, corresponding a) Would a 90% confidence interval be wider or narto a value of 710.5 for Michelson’s 1897 measurerower than the 98% confidence interval you calculated ments. What does this indicate about Michelson’s two before? Explain. (You should not need to compute the experiments? Explain, using your confidence interval. new interval.) T 19. Departures 2011 What are the chances your flight will b) What are the advantages and disadvantages of the 98% leave on time? The U.S. Bureau of Transportation Statisconfidence interval? tics of the Department of Transportation publishes inforc) If we conduct further research, this time using a sammation about airline performance. Here are a histogram ple of 500 adults, how would you expect the 98% conand summary statistics for the percentage of flights defidence interval to change? Explain. parting on time each month from 1995 thru September d) How large a sample would you need to estimate the 2011. (www.transtats.bts.gov/HomeDrillChart.asp) mean body temperature to within 0.05 degrees with 98% confidence? n y s 20 # of Months 16. Parking II Suppose that, for budget planning purposes, the city in Exercise 14 needs a better estimate of the mean daily income from parking fees. a) Someone suggests that the city use its data to create a 95% confidence interval instead of the 90% interval first created. How would this interval be better for the city? (You need not actually create the new interval.) b) How would the 95% interval be worse for the planners? c) How could they achieve an interval estimate that would better serve their planning needs? d) How many days’ worth of data must they collect to have 95% confidence of estimating the true mean to within $3? 15 201 80.752 4.594 10 5 65 70 75 80 OT Departure (%) 85 90 There is no evidence of a trend over time. a) Check the assumptions and conditions for inference. b) Find a 90% confidence interval for the true percentage of flights that depart on time. c) Interpret this interval for a traveller planning to fly. d) Suppose the number of flights differs considerably from month to month. What are you actually estimating in part b)? What might you recommend doing instead? 17. Speed of light In 1882, Michelson measured the speed of light (usually denoted c as in Einstein’s famous equation E = mc2). His values are in km/sec and have 299 000 subtracted from them. He reported the results of 23 trials with a mean of 756.22 and a standard deviation of 107.12. a) Find a 95% confidence interval for the true speed of T 20. Arrivals 2011 Will your flight get you to your destinalight from these statistics. tion on time? The U.S. Bureau of Transportation Statisb) State in words what this interval means. Keep in mind tics reported the percentage of flights that were late each that the speed of light is a physical constant that, as far month from 1995 through September of 2011. Here’s a as we know, has a value that is true throughout the histogram, along with some summary statistics: universe. c) What assumptions must you make in order to use your n 201 30 method? y 17.111 25 speed of light (described in Exercise 17), Michelson conducted an “improved” experiment. In 1897, he reported results of 100 trials with a mean of 852.4 km/sec and a standard deviation of 79.0. a) What is the standard error of the mean for these data? b) Without computing it, how would you expect a 95% confidence interval for the second experiment to differ from the confidence interval for the first? Note at least three specific reasons why they might differ, and indicate the ways in which these differences would change the interval. # of Months T 18. Better light After his first attempt to determine the s 3.895 20 15 10 5 10 15 20 Late Arrival (%) 25 M20_DEVE8422_02_SE_C20.indd Page 593 30/07/14 7:07 PM f-w-147 /206/PHC00112/9780321828422_DEVEAUX/DEVEAUX_DATA_AND_MODELS2ce_SE_9780321828422/S .. CHAPTER 20 Inferences About Means We can consider these data to be a representative sample of all months. There is no evidence of a time trend. a) Check the assumptions and conditions for inference about the mean. b) Find a 99% confidence interval for the true percentage of flights that arrive late. c) Interpret this interval for a traveller planning to fly. d) The t test (or confidence interval) is sometimes referred to as a “small sample” procedure. Why would it be okay to use a z* value instead of a t* value in constructing your confidence interval in part b)? T 21. Farmed salmon, second look This chapter’s For Ex- amples looked at mirex contamination in farmed salmon. We first found a 95% confidence interval for the mean concentration to be 0.0834 to 0.0992 parts per million. Later, we rejected the null hypothesis that the mean did not exceed the EPA’s recommended safe level of 0.08 ppm based on a P-value of 0.0027. Explain how these two results are consistent. Your explanation should discuss the confidence level, the P-value, and the decision. 22. Hot dogs A nutrition lab tested 40 hot dogs to see if their mean sodium content was less than the 325 mg upper limit set by regulations for “reduced sodium” franks. The lab failed to reject the null hypothesis that the hot dogs did not meet this requirement, with a P-value of 0.142. A 90% confidence interval estimated the mean sodium content for this kind of hot dog at 317.2 to 326.8 mg. Explain how these two results are consistent. Your explanation should discuss the confidence level, the P-value, and the decision. 23. Pizza A researcher tests whether the mean cholesterol level among those who eat frozen pizza exceeds the value considered to indicate a health risk. She gets a P-value of 0.07. Explain in this context what the “7%” represents. 24. Golf balls The United States Golf Association (USGA) sets performance standards for golf balls. For example, the initial velocity of the ball may not exceed 250 feet per second when measured by an apparatus approved by the USGA. Suppose a manufacturer introduces a new kind of ball and provides a sample for testing. Based on the mean speed in the test, the USGA comes up with a P-value of 0.34. Explain in this context what the “34%” represents. 25. TV safety The manufacturer of a metal stand for home television sets must be sure that its product will not fail under the weight of the television. Since some larger sets weigh nearly 300 pounds (about 136 kg), the company’s safety inspectors have set a standard of ensuring that the stands can support an average of over 500 pounds. Their inspectors regularly subject a random sample of the 593 stands to increasing weight until they fail. They test the hypothesis H0: m = 500 against HA: m 7 500, using the level of significance a = 0.01. If the stands in the sample fail to pass this safety test, the inspectors will not certify the product for sale to the general public. a) Is this an upper-tail or lower-tail test? In the context of the problem, why do you think this is important? b) Explain what will happen if the inspectors commit a Type I error. c) Explain what will happen if the inspectors commit a Type II error. 26. Catheters During an angiogram, heart problems can be examined via a small tube (a catheter) threaded into the heart from a vein in the patient’s leg. It’s important that the company that manufactures the catheter maintain a diameter of 2.00 mm. (The standard deviation is quite small.) Each day, quality control personnel make several measurements to test H0: m = 2.00 against HA: m ≠ 2.00 at a significance level of a = 0.05. If they discover a problem, they will stop the manufacturing process until it is corrected. a) Is this a one-sided or two-sided test? In the context of the problem, why do you think this is important? b) Explain in this context what happens if the quality control people commit a Type I error. c) Explain in this context what happens if the quality control people commit a Type II error. 27. TV safety revisited The manufacturer of the metal television stands in Exercise 25 is thinking of revising its safety test. a) If the company’s lawyers are worried about being sued for selling an unsafe product, should they increase or decrease the value of a? Explain. b) In this context, what is meant by the power of the test? c) If the company wants to increase the power of the test, what options does it have? Explain the advantages and disadvantages of each option. 28. Catheters again The catheter company in Exercise 26 is reviewing its testing procedure. a) Suppose the significance level is changed to a = 0.01. Will the probability of a Type II error increase, decrease, or remain the same? b) What is meant by the power of the test the company conducts? c) Suppose the manufacturing process is slipping out of proper adjustment. As the actual mean diameter of the catheters produced gets farther and farther above the desired 2.00 mm, will the power of the quality control test increase, decrease, or remain the same? d) What could they do to improve the power of the test? 29. Marriage In 1960, census results indicated that the age at which Canadian women first married had a mean of 22.6 years. It is widely suspected that young people today are waiting longer to get married. We want to find M20_DEVE8422_02_SE_C20.indd Page 594 30/07/14 7:07 PM f-w-147 594 /206/PHC00112/9780321828422_DEVEAUX/DEVEAUX_DATA_AND_MODELS2ce_SE_9780321828422/S .. PART VI Accessing Associations Between Variables out if the mean age of first marriage has increased durd) Explain in context what your interval means. ing the past 40 years. e) Comment on the company’s stated net weight of a) Write appropriate hypotheses. 28.3 grams. b) We plan to test our hypotheses by selecting a random T 33. Popcorn Yvon Hopps ran an experiment to test sample of 40 women who married for the first time optimum power and time settings for microwave last year. Do you think the necessary assumptions for popcorn. His goal was to find a combination of power inference are satisfied? Explain. and time that would deliver high-quality popcorn c) Describe the approximate sampling distribution model with only 10% of the kernels left unpopped, on for the mean age in such samples. average. After experimenting with several bags, d) The women in our sample married at an average age of he determined that power 9 at four minutes was the 27.2 years, with a standard deviation of 5.3 years. best combination. What is the P-value for this result? a) He concluded that this popping method achieved the e) Explain (in context) what this P-value means. 10% goal. If it really does not work that well, what f) What is your conclusion? kind of error did Hopps make? b) To be sure that the method was successful, he popped 30. Fuel economy A company with a large fleet of cars eight more bags of popcorn (selected at random) at hopes to keep gasoline costs down and sets a goal of atthis setting. All were of high quality, with the followtaining a fleet average of at most 9 litres per 100 km. To ing percentages of unpopped popcorn: 7, 13.2, 10, 6, see if the goal is being met, they check the gasoline us7.8, 2.8, 2.2, 5.2. Does this provide evidence that he age for 50 company trips chosen at random, finding a met his goal of an average of no more than 10% unmean of 9.40 L/100 km and a standard deviation of popped kernels? Explain. 1.81 L/100 km. Is this strong evidence that they have failed to attain their fuel economy goal? T 34. Ski wax Bjork Larsen was trying to decide whether to a) Write appropriate hypotheses. use a new racing wax for cross-country skis. He deb) Are the necessary assumptions to make inferences cided that the wax would be worth the price if he could satisfied? average less than 55 seconds on a course he knew well, c) Describe the sampling distribution model of mean fuel so he planned to test the wax by racing on the course economy for samples like this. eight times. d) Find the P-value. a) Suppose that he eventually decides not to buy the e) Explain what the P-value means in this context. wax, but it really would lower his average time to f) State an appropriate conclusion. below 55 seconds. What kind of error would he T 31. Ruffles Students investigating the packaging of potato chips purchased six bags of Lay’s Ruffles marked with a net weight of 28.3 grams. They carefully weighed the contents of each bag, recording the following weights (in grams): 29.3, 28.2, 29.1, 28.7, 28.9, 28.5. a) Do these data satisfy the assumptions for inference? Explain. b) Find the mean and standard deviation of the observed weights. c) Create a 95% confidence interval for the mean weight of such bags of chips. d) Explain in context what your interval means. e) Comment on the company’s stated net weight of 28.3 grams. T 32. Doritos Some students checked six bags of Doritos marked with a net weight of 28.3 grams. They carefully weighed the contents of each bag, recording the following weights (in grams): 29.2, 28.5, 28.7, 28.9, 29.1, 29.5. a) Do these data satisfy the assumptions for inference? Explain. b) Find the mean and standard deviation of the observed weights. c) Create a 95% confidence interval for the mean weight of such bags of chips. have made? b) His eight race times were 56.3, 65.9, 50.5, 52.4, 46.5, 57.8, 52.2, and 43.2 seconds. Should he buy the wax? Explain. T 35. Chips Ahoy In 1998, as an advertising campaign, the Nabisco Company announced a “1000 Chips Challenge,” claiming that every 18-ounce (about 625 grams) bag of their Chips Ahoy cookies contained at least 1000 chocolate chips. Dedicated Statistics students at the Air Force Academy (no kidding) purchased some randomly selected bags of cookies and counted the chocolate chips. Some of their data are given below. (Chance, 12, no. 1[1999]) 1219 1214 1087 1200 1419 1121 1325 1345 1244 1258 1356 1132 1191 1270 1295 1135 a) Check the assumptions and conditions for inference. Comment on any concerns you have. b) Create a 95% confidence interval for the average number of chips in bags of Chips Ahoy cookies. c) What does this evidence say about Nabisco’s claim? Use your confidence interval to test an appropriate hypothesis and state your conclusion. M20_DEVE8422_02_SE_C20.indd Page 595 05/08/14 6:51 PM f-w-147 /206/PHC00112/9780321828422_DEVEAUX/DEVEAUX_DATA_AND_MODELS2ce_SE_9780321828422/S .. CHAPTER 20 Inferences About Means T 36. Yogourt Consumer Reports tested 14 brands of vanilla yogourt and found the following numbers of calories per serving: 160 130 200 170 220 190 230 80 120 120 180 100 140 170 595 for braking effectiveness. The company hopes the tire will allow a car travelling at 100 km/h to come to a complete stop within an average of 38 metres after the brakes are applied. They will adopt the new tread pattern unless there is strong evidence that the tires do not meet this objective. The distances (in metres) for 10 stops on a test track were 39.3, 39.0. 39.6, 40.2, 41.1, 37.5, 31.1, 38.1, 39.0, and 39.6. Should the company adopt the new tread pattern? Test an appropriate hypothesis and state your conclusion. Explain how you dealt with the outlier and why you made the recommendation you did. # of Golfers a) Check the assumptions and conditions for inference. b) Create a 95% confidence interval for the average calorie content of vanilla yogourt. c) A diet guide claims that you will get an average of 120 calories from a serving of vanilla yogourt. What does this evidence indicate? Use your confidence interval to T 39. Driving distance 2011 How far do professional golfers test an appropriate hypothesis and state your conclusion. drive a ball? (For non-golfers, the drive is the shot hit d) *Perform a sign-test to test the hypothesis that the from a tee at the start of a hole and is typically the lonmedian number of calories is 120. Is your conclusion gest shot.) Here’s a histogram of the average driving dissimilar to what you found in part c)? tances of the 186 leading professional golfers by end of November 2011 along with summary statistics (www. T 37. Maze Psychology experiments sometimes involve testing pgatour.com). the ability of rats to navigate mazes. The mazes are classified according to difficulty, as measured by the mean Count 186 40 length of time it takes rats to find the food at the end. One Mean 291.09 yd researcher needs a maze that will take rats an average of StdDev 8.343 yd 30 about one minute to solve. He tests one maze on several 20 rats, collecting the data shown. 10 Time (sec) 38.4 57.6 46.2 55.5 62.5 49.5 38.0 40.9 62.8 44.3 33.9 93.8 50.4 47.9 35.0 69.2 52.8 46.2 60.1 56.3 55.1 a) Plot the data. Do you think the conditions for inference are satisfied? Explain. b) Test the hypothesis that the mean completion time for this maze is 60 seconds. What is your conclusion? c) Eliminate the outlier, and test the hypothesis again. What is your conclusion? d) Do you think this maze meets the “one-minute average” requirement? Explain. e) *Perform a sign-test to see if the median time is one minute or less, keeping the outlier in the data set. Does your conclusion change from the one you arrived at in part d)? 38. Braking A tire manufacturer is considering a newly designed tread pattern for its all-weather tires. Tests have indicated that these tires will provide better gas mileage and longer tread life. The last remaining test is 255 270 285 300 Driving Distance (yards) a) Find a 95% confidence interval for the mean drive distance. b) Interpreting this interval raises some problems. Discuss. c) The data are the mean driving distance for each golfer. Is that a concern in interpreting the interval? (Hint: Review the What Can Go Wrong warnings of Chapter 8. Chapter 8?! Yes, Chapter 8.) d) If instead we used these golfers’ individual drive distances, what problem would this create for our inferential procedures? T 40. Wind power Should you generate electricity with your own personal wind turbine? That depends on whether you have enough wind on your site. To produce enough energy, your site should have an annual average wind speed of at least eight miles per hour (mph), according to the Wind Energy Association. One candidate site was monitored for a year, with wind speeds recorded every six hours. A total of 1114 readings of wind speed averaged 8.019 mph with a standard deviation of 3.813 mph. You’ve been asked to make a statistical report to help the landowner decide whether to place a wind turbine at this site. a) Discuss the assumptions and conditions for using Student’s t inference methods with these data. Here M20_DEVE8422_02_SE_C20.indd Page 596 30/07/14 7:07 PM f-w-147 596 /206/PHC00112/9780321828422_DEVEAUX/DEVEAUX_DATA_AND_MODELS2ce_SE_9780321828422/S .. PART VI Accessing Associations Between Variables are some plots that may help you decide whether the methods can be used: # of Readings 150 100 50 0 5 10 15 20 Wind Speed (mph) Wind Speed (mph) 20 15 10 5 0 Wind Speed (mph) –2 0 nscores a) Estimate with a 95% confidence interval the true mean percentage increase in the number of unemployed persons per CMA/CA in Canada over this period. b) Is your 95% confidence level quoted in part a) trustworthy? Check what you should. c) The overall Canadian change in unemployment numbers was an increase of 71.5%. If we took similar repeated random samples and calculated such 95% confidence intervals over and over, would you expect them to catch this 71.5% figure 95% of the time? Why or why not? 2 20 15 10 5 250 500 Time 750 Area % Change Victoria 182.8 Alma 10.4 Salaberry-de-Valleyfield 64.0 Penticton 180.0 Campbell River 139.6 Woodstock 127.1 Baie-Comeau 6.6 Whitehorse 62.2 Hawkesbury, Ont. part 60.0 London 96.5 Prince Albert 62.2 Red Deer 304.3 Swift Current 200.0 Port Hope 122.2 Port Alberni 102.7 Ottawa-Gatineau, Gatineau part 53.7 Norfolk 129.3 Trois-Rivières 29.3 Labrador City 110.3 Nanaimo 136.6 Source: Adapted from Statistics Canada, Employment Insurance Statistics Maps, 73-002-XWE2009002 June 2009, Released August 25, 2009. 1000 b) What would you tell the landowner about whether this site is suitable for a small wind turbine? Explain. c) Why could we easily analyze data like this even before Gosset’s discovery of the t distribution? 41. Worst of times Below is a sample randomly selected from all the Census Metropolitan Areas (CMAs) and Census agglomerations (CAs) in Canada showing the percentage change in the number of persons unemployed between May 2008 and May 2009 (during the deep 2008–2009 recession) for each area. 42. Mercury sushi Torontonians (including one of your authors) seem to love their sushi, but is it always safe? The New York Times bought pieces of tuna sushi from a number of restaurants and stores in New York City in October 2007 and tested them for mercury levels. The results were not good. At most, consuming just six pieces per week would put you beyond an acceptable consumption level of mercury (49 micrograms of mercury per week for a person of average weight of 70 kg). Let’s hope Toronto would fare better—but then again, the article states that experts believe similar results would be observed elsewhere, particularly for bluefin tuna sushi (the most common type in the survey). Analysts examined at least two pieces from each place and calculated the methylmercury level in parts per million. Results below are for the piece of sushi with the highest mercury level for the restaurants surveyed. The pieces vary in size, M20_DEVE8422_02_SE_C20.indd Page 597 30/07/14 7:07 PM f-w-147 /206/PHC00112/9780321828422_DEVEAUX/DEVEAUX_DATA_AND_MODELS2ce_SE_9780321828422/S .. CHAPTER 20 Inferences About Means so also shown is how many pieces per week it would take to exceed the acceptable mercury intake of 49 micrograms per week. Methylmercury Number of pieces Restaurants (parts per million) to reach Rfd Bar Masa 0.49 8.6 Blue Ribbon Sushi 1.40 2.6 Japonica 0.86 1.6 Jewel Bako 0.83 5.2 Megu 0.87 7.7 Monster Sushi 0.56 4.7 (22 West 46th Street) New York Times 0.50 6.4 cafeteria Nobu Next Door 1.00 6.2 Sushi of Gari 1.04 3.6 Sushi Seki 1.04 4.9 Sushi Yasuda 0.79 9.9 Yuka 0.61 3.3 Yuki Sushi 0.86 4.1 Source: From the New York Times, January 23, 2008, © 2008 The New York Times. All rights reserved. Used by permission and protected by the copyright laws of the United States. The printing, copying, redistribution, or retransmission of this content without express written permission is prohibited. www.newyorktimes.com. a) Give a 95% confidence interval for the mean mercury concentration level (per worst piece) if we can consider this to be a representative sample of New York City restaurants. Now check to see if that figure of 95% confidence is really trustworthy (that is, check and comment on any necessary conditions). b) Give a 95% confidence interval for the mean number of (worst) pieces required to exceed health guidelines if we can assume this to be a representative sample of New York City restaurants. Now check to see if that 95% confidence level figure is really trustworthy (that is, check and comment on any necessary conditions). 43. Simulations Use your computer software to generate a sample of size 20 from a Normal distribution with a mean of 50 and a standard deviation of 10. a) From the sample, calculate a 90% confidence interval for the population mean. Does it contain the number 50? b) Repeat part a) for 99 fresh samples. How many confidence intervals out of 100 contained the number 50? What percent of confidence intervals would you expect to contain the number 50 if you repeated these simulations many times? If X = the number of confidence intervals out of 100 that contain 50, what is the distribution of X? 597 44. More simulations Use your computer software to generate a sample of size 30 from a (continuous) uniform distribution on the interval 0 to 1. a) From the sample, calculate an 80% confidence interval for the population mean. Does it contain the true mean? b) Repeat part a) for 49 fresh samples. How many confidence intervals out of 50 contained the true mean? What percent of confidence intervals would you expect to contain the true mean if you repeated these simulations many times? If X = the number of confidence intervals out of 50 that contain the true mean of this uniform distribution, what is the distribution of X? 45. Still more simulations Use your computer software to generate a sample of size 15 from an exponential distribution with a mean of 1 (if a parameter is requested, set it equal to 1.0). Plot the data and describe the shape of this distribution. a) From the sample, calculate a 90% confidence interval for the population mean. Does it contain the true mean? b) Repeat part a) for 99 fresh samples. How many confidence intervals out of 100 contained the true mean of 1.0? What percent of confidence intervals would you expect to contain 1.0 if you repeated these simulations many times? If there are difficulties answering this question, explain. If we changed the sample size to 100, would that affect your answer? 46. Yet more simulation. Use your computer software to generate a sample of size 100 from an exponential distribution with a mean of 1 (if requested, set scale = 1.0 and threshold = 0.0). Plot the data and describe the shape of this distribution. a) From the sample, calculate a 90% confidence interval for the population mean. Does it contain the true mean? b) Repeat part a) for 99 fresh samples. How many confidence intervals out of 100 contained the true mean of 1.0? What percent of confidence intervals would you expect to contain 1.0 if you repeated these simulations many times? Justify your answer. 47. Even more simulations Use your computer software to generate a sample of size 5 from a Normal distribution with mean of 50 and a standard deviation of 5. (For example, these might be Guinness stout measurements for a batch that you are checking for adequate quality.) a) From the sample, test the null hypothesis that the population mean is 50 (our requirement for passing the batch) at the 10% significance level versus a two-sided alternative. Did you reject the null hypothesis? Did you make an incorrect decision? Did you pass or fail a good or bad batch of stout? An incorrect decision here would constitute what type of error? M20_DEVE8422_02_SE_C20.indd Page 598 30/07/14 7:07 PM f-w-147 598 /206/PHC00112/9780321828422_DEVEAUX/DEVEAUX_DATA_AND_MODELS2ce_SE_9780321828422/S .. PART VI Accessing Associations Between Variables b) Repeat part a) for 99 fresh samples. In how many tests did you reject the null hypothesis? In what percent of tests would you expect to reject this null hypothesis at the 10% level if you repeated these simulations many times? If X = the number of tests out of 100 in which you reject the null hypothesis at the 10% significance level, what is the distribution of X? 48. Final simulations Use your computer software to generate a sample of size 5 from a Normal distribution with a mean of 50 and a standard deviation of 5. (For example, these might be Guinness stout measurements for a batch that you are checking for adequate quality.) a) From the sample, test the null hypothesis that the population mean is 60 (our requirement for passing the batch) at the 10% significance level versus a two-sided alternative. Did you reject the null hypothesis? Was this a correct decision or an error? Did you pass or fail a good or bad batch of stout? What type of error would a wrong decision here constitute? b) Repeat part a) for 99 fresh samples. In how many tests did you reject the null hypothesis? This number is an estimate of something—what, exactly? If you have the appropriate software, use it to determine what would be the long-run percentage of such rejections. 49. Calculating power For the chapter example about salmon mirex levels, let’s do an approximate calculation of the power of the test for an alternative of 0.09 ppm. For samples of size n Ú 30, we can approximate the t distribution by the standard Normal distribution in these rough calculations. We also need to make a guess at the value of the unknown parameter s, but here a study has already been run, so let’s just take that s = 0.0495 as the guess for s and round it to 0.05. Since we are guessing at s, this calculation is just an approximation, but usually that’s all we need. a) Setting alpha at 0.05, find the critical value for a standard Normal z-statistic. Write the criterion for rejection of the null in terms of the t-statistic. You should y - 0.08 7 z*, with a specific have a criterion like s> 1150 number for the critical value. b) Now another approximation. Pretend that the sample standard deviation s will equal the true population standard deviation s. Funny thing to do, since s is random, not a constant, but this works well enough as an approximation when n is not too small (and otherwise we’d be stuck!). Rewrite the criterion above with just the sample mean on the left side, that is, find out just how big the sample mean must be for you to reject the null hypothesis (after setting s = s). You should have a criterion like y 7 y* with a specific number for the y* critical value. c) Calculate the probability that y is bigger than y*, assuming the true mean is equal to 0.09 (standardize properly and use the standard Normal table). What you’ve now got is the power—the probability of making the right decision (to reject m = 0.08 ppm) should the true mean m = 0.09 ppm. d) For a small n, though, this does not work well, since you have to take into account more properly the random variation present in the sample standard deviation s, in which case using statistical software is recommended. If your software does power calculations for the one-sample t-test, use it to confirm your calculation above. Also using your software, determine how low the power drops: i. if you halve your sample size (n = 75). ii. if you halve the sample size yet again (to n = 38). 50. More power to you For the chapter example about school travel times, let’s do an approximate calculation of the power of the test for an alternative of 20 minutes. For samples of size n Ú 30, we can approximate the t distribution by the standard Normal distribution in these rough calculations. We also need to make a guess at the value of the unknown parameter s, but here, a study has already been run so let’s just make that s = 9.66 minutes as the guess for s and round it to 10 minutes. Since we are guessing at s, this calculation is just an approximation, but usually that’s all we need. a) Setting alpha at 0.05, find the critical value for a standard Normal z-statistic. Write the criterion for rejection of the null in terms of the t-statistic. You y - 15 should have a criterion like 7 z*, with a s> 140 specific number for the critical value. b) Now another approximation. Pretend that the sample standard deviation s will equal the true population standard deviation s. Funny thing to do, since s is random, not a constant, but this works well enough as an approximation when n is not too small (and otherwise we’d be stuck!). Rewrite the criterion above with just the sample mean on the left side; that is, find out just how big the sample mean must be for you to reject the null hypothesis (after setting s = s). You should have a criterion like y 7 y* with a specific number for the y* critical value. c) Calculate the probability that y is bigger than y*, assuming the true mean is equal to 20 minutes (standardize properly and use the standard Normal table). What you’ve now got is the power—the probability of making the right decision (to reject m = 15 minutes) should the true mean m = 20 minutes. d) For small n, this does not work well, since you have to take into account more properly the random variation present in the sample standard deviation s, in which case using statistical software is recommended. If your software does power calculations for the one-sample ttest, use it to confirm your calculation above. Also using your software, determine how low the power drops: i. if you halve the sample size (n = 20). ii. if you double the sample size (to n = 80). M20_DEVE8422_02_SE_C20.indd Page 599 30/07/14 7:07 PM f-w-147 /206/PHC00112/9780321828422_DEVEAUX/DEVEAUX_DATA_AND_MODELS2ce_SE_9780321828422/S .. CHAPTER 20 Inferences About Means Just Checking 599 ANSWERS 1. Questions on the short form are answered by everyone in the population. This is a census, so means or proportions are the true population values. The long forms are given to just a sample of the population. When we estimate parameters from a sample, we use a confidence interval to take sample-to-sample variability into account. 2. They don’t know the population standard deviation, so they must use the sample standard deviation as an estimate. The additional uncertainty is taken into account by t-models if we are using an unweighted average.21 We don’t know what model to use for a weighted average (perhaps a t model but with a different SE formula). s 3. The margin of error for a confidence interval for a mean depends, in part, on the standard error, SE(y) = . 1n Since n is in the denominator, smaller sample sizes lead to larger SEs and correspondingly wider intervals. Long forms returned by one in every five households in a less populous area will produce a smaller sample. 4. The t* value would change a little, while n and its square root change a lot, making the interval much narrower for the larger sample. The smaller sample is one fourth as large, so the confidence interval would be roughly twice as wide. 5. We expect 95% of such intervals to cover the true value, so five of the 100 intervals might be expected to miss. 6. The power would increase if we have a larger sample size. Go to MathXL at www.mathxl.com or MyStatLab at www.mystatlab.com. You can practise exercises for this chapter as often as you want. The guided solutions will help you find answers step by step. You’ll find a personalized study plan available to you too! 21 Though ideally a finite population correction factor should be applied to the SE formula, as discussed in Chapter 15, since the sample is more than 10% of the population.