1 FOR BUSINESS AND ECONOMICS STATISTICS CHAPTER 9 Hypothesis Tests CONTENTS STATISTICS IN PRACTICE: JOHN MORRELL & COMPANY 9.1 DEVELOPING NULL AND ALTERNATIVE HYPOTHESES Testing Research Hypotheses Testing the Validity of a Claim Testing in Decision-Making Situations Summary of Forms for Null and Alternative Hypotheses 9.2 TYPE I AND TYPE II ERRORS 9.3 POPULATION MEAN: σ KNOWN One-Tailed Test Two-Tailed Test Summary and Practical Advice Relationship Between Interval Estimation and Hypothesis Testing 9.4 POPULATION MEAN: σ UNKNOWN One-Tailed Test Two-Tailed Test Summary and Practical Advice 9.5 POPULATION PROPORTION Summary 9.6 HYPOTHESIS TESTING AND DECISION MAKING 9.7 CALCULATING THE PROBABILITY OF TYPE II ERRORS 9.8 DETERMINING THE SAMPLE SIZE FOR HYPOTHESIS TESTS ABOUT A POPULATION MEAN
2 Chapter 9 Hypothesis Tests 337 STATISTICS in PRACTICE JOHN MORRELL & COMPANY* CINCINNATI, OHIO John Morrell & Company, which began in England in 1827, is considered the oldest continuously operating meat manufacturer in the United States. It is a wholly owned and independently managed subsidiary of Smithfield Foods, Smithfield, Virginia. John Morrell & Company offers an extensive product line of processed meats and fresh pork to consumers under 13 regional brands including John Morrell, E-Z-Cut, Tobin s First Prize, Dinner Bell, Hunter, Kretschmar, Rath, Rodeo, Shenson, Farmers Hickory Brand, Iowa Quality, and Peyton s. Each regional brand enjoys high brand recognition and loyalty among consumers. Market research at Morrell provides management with upto-date information on the company s various products and how the products compare with competing brands of similar products. A recent study investigated consumer preference for Morrell s Convenient Cuisine Beef Pot Roast compared to similar beef products from two major competitors. In the three-product comparison test, a sample of consumers was used to indicate how the products rated in terms of taste, appearance, aroma, and overall preference. One research question concerned whether Morrell s Convenient Cuisine Beef Pot Roast was the preferred choice of more than 50% of the consumer population. Letting p indicate the population proportion preferring Morrell s product, the hypothesis test for the research question is as follows: H 0 : p.50 H a : p.50 The null hypothesis H 0 indicates the preference for Morrell s product is less than or equal to 50%. If the sample data sup- *The authors are indebted to Marty Butler, vice president of Marketing, John Morrell, for providing this Statistics in Practice. Convenient Cuisine fully-cooked entrees allow consumers to heat and serve in the same microwaveable tray. Courtesy of John Morrell s Convenient Cuisine products. port rejecting H 0 in favor of the alternate hypothesis H a, Morrell will draw the research conclusion that in a three-product comparison, their product is preferred by more than 50% of the consumer population. In an independent taste test study using a sample of 224 consumers in Cincinnati, Milwaukee, and Los Angeles, 150 consumers selected the Morrell Convenient Cuisine Beef Pot Roast as the preferred product. Using statistical hypothesis testing procedures, the null hypothesis H 0 was rejected. The study provided statistical evidence supporting H a and the conclusion that the Morrell product is preferred by more than 50% of the consumer population. The point estimate of the population proportion was p 150/ Thus, the sample data provided support for a food magazine advertisement showing that in a three-product taste comparison, Morrell s Convenient Cuisine Beef Pot Roast was preferred 2 to 1 over the competition. In this chapter we will discuss how to formulate hypotheses and how to conduct tests like the one used by Morrell. Through the analysis of sample data, we will be able to determine whether a hypothesis should or should not be rejected. In Chapters 7 and 8 we showed how a sample could be used to develop point and interval estimates of population parameters. In this chapter we continue the discussion of statistical inference by showing how hypothesis testing can be used to determine whether a statement about the value of a population parameter should or should not be rejected. In hypothesis testing we begin by making a tentative assumption about a population parameter. This tentative assumption is called the null hypothesis and is denoted by H 0. We then define another hypothesis, called the alternative hypothesis, which is the opposite of what is stated in the null hypothesis. The alternative hypothesis is denoted by H a.
3 338 STATISTICS FOR BUSINESS AND ECONOMICS The hypothesis testing procedure uses data from a sample to test the two competing statements indicated by H 0 and H a. This chapter shows how hypothesis tests can be conducted about a population mean and a population proportion. We begin by providing examples that illustrate approaches to developing null and alternative hypotheses. 9.1 Developing Null and Alternative Hypotheses Learning to formulate hypotheses correctly will take practice. Expect some initial confusion over the proper choice for H 0 and H a. The examples in this section show a variety of forms for H 0 and H a depending upon the application. In some applications it may not be obvious how the null and alternative hypotheses should be formulated. Care must be taken to structure the hypotheses appropriately so that the hypothesis testing conclusion provides the information the researcher or decision maker wants. Guidelines for establishing the null and alternative hypotheses will be given for three types of situations in which hypothesis testing procedures are commonly employed. Testing Research Hypotheses Consider a particular automobile model that currently attains an average fuel efficiency of 24 miles per gallon. A product research group developed a new fuel injection system specifically designed to increase the miles-per-gallon rating. To evaluate the new system, several will be manufactured, installed in automobiles, and subjected to research-controlled driving tests. Here the product research group is looking for evidence to conclude that the new system increases the mean miles-per-gallon rating. In this case, the research hypothesis is that the new fuel injection system will provide a mean miles-per-gallon rating exceeding 24; that is, µ 24. As a general guideline, a research hypothesis should be stated as the alternative hypothesis. Hence, the appropriate null and alternative hypotheses for the study are: H 0 : µ 24 H a : µ 24 The conclusion that the research hypothesis is true is made if the sample data contradict the null hypothesis. If the sample results indicate that H 0 cannot be rejected, researchers cannot conclude that the new fuel injection system is better. Perhaps more research and subsequent testing should be conducted. However, if the sample results indicate that H 0 can be rejected, researchers can make the inference that H a : µ 24 is true. With this conclusion, the researchers gain the statistical support necessary to state that the new system increases the mean number of miles per gallon. Production with the new system should be considered. In research studies such as these, the null and alternative hypotheses should be formulated so that the rejection of H 0 supports the research conclusion. The research hypothesis therefore should be expressed as the alternative hypothesis. Testing the Validity of a Claim As an illustration of testing the validity of a claim, consider the situation of a manufacturer of soft drinks who states that it fills two-liter containers of its products with an average of at least 67.6 fluid ounces. A sample of two-liter containers will be selected, and the contents will be measured to test the manufacturer s claim. In this type of hypothesis testing situation, we generally assume that the manufacturer s claim is true unless the sample evidence is contradictory. Using this approach for the soft-drink example, we would state the null and alternative hypotheses as follows. H 0 : µ 67.6 H a : µ 67.6
4 Chapter 9 Hypothesis Tests 339 A manufacturer s claim is usually given the benefit of the doubt and stated as the null hypothesis. The conclusion that the claim is false can be made if the null hypothesis is rejected. If the sample results indicate H 0 cannot be rejected, the manufacturer s claim will not be challenged. However, if the sample results indicate H 0 can be rejected, the inference will be made that H a : µ 67.6 is true. With this conclusion, statistical evidence indicates that the manufacturer s claim is incorrect and that the soft-drink containers are being filled with a mean less than the claimed 67.6 ounces. Appropriate action against the manufacturer may be considered. In any situation that involves testing the validity of a claim, the null hypothesis is generally based on the assumption that the claim is true. The alternative hypothesis is then formulated so that rejection of H 0 will provide statistical evidence that the stated assumption is incorrect. Action to correct the claim should be considered whenever H 0 is rejected. Testing in Decision-Making Situations In testing research hypotheses or testing the validity of a claim, action is taken if H 0 is rejected. In many instances, however, action must be taken both when H 0 cannot be rejected and when H 0 can be rejected. In general, this type of situation occurs when a decision maker must choose between two courses of action, one associated with the null hypothesis and another associated with the alternative hypothesis. For example, on the basis of a sample of parts from a shipment just received, a quality control inspector must decide whether to accept the shipment or to return the shipment to the supplier because it does not meet specifications. Assume that specifications for a particular part require a mean length of two inches per part. If the mean length is greater or less than the two-inch standard, the parts will cause quality problems in the assembly operation. In this case, the null and alternative hypotheses would be formulated as follows. H 0 : µ 2 H a : µ 2 If the sample results indicate H 0 cannot be rejected, the quality control inspector will have no reason to doubt that the shipment meets specifications, and the shipment will be accepted. However, if the sample results indicate H 0 should be rejected, the conclusion will be that the parts do not meet specifications. In this case, the quality control inspector will have sufficient evidence to return the shipment to the supplier. Thus, we see that for these types of situations, action is taken both when H 0 cannot be rejected and when H 0 can be rejected. Summary of Forms for Null and Alternative Hypotheses The hypothesis tests in this chapter involve two population parameters: the population mean and the population proportion. Depending on the situation, hypothesis tests about a population parameter may take one of three forms: two use inequalities in the null hypothesis; the third uses an equality in the null hypothesis. For hypothesis tests involving a population mean, we let µ 0 denote the hypothesized value and we must choose one of the following three forms for the hypothesis test. The three possible forms of hypotheses H 0 and H a are shown here. Note that the equality always appears in the null hypothesis H 0. H 0 : µ µ 0 H a : µ µ 0 H 0 : µ µ 0 H a : µ µ 0 H 0 : µ µ 0 H a : µ µ 0 For reasons that will be clear later, the first two forms are called one-tailed tests. The third form is called a two-tailed test. In many situations, the choice of H 0 and H a is not obvious and judgment is necessary to select the proper form. However, as the preceding forms show, the equality part of the expression (either,, or ) always appears in the null hypothesis. In selecting the proper
5 340 STATISTICS FOR BUSINESS AND ECONOMICS form of H 0 and H a, keep in mind that the alternative hypothesis is often what the test is attempting to establish. Hence, asking whether the user is looking for evidence to support µ µ 0, µ µ 0,orµ µ 0 will help determine H a. The following exercises are designed to provide practice in choosing the proper form for a hypothesis test involving a population mean. Exercises 1. The manager of the Danvers-Hilton Resort Hotel stated that the mean guest bill for a weekend is $600 or less. A member of the hotel s accounting staff noticed that the total charges for guest bills have been increasing in recent months. The accountant will use a sample of weekend guest bills to test the manager s claim. a. Which form of the hypotheses should be used to test the manager s claim? Explain. H 0 : µ 600 H a : µ 600 H 0 : µ 600 H a : µ 600 H 0 : µ 600 H a : µ 600 SELF test b. What conclusion is appropriate when H 0 cannot be rejected? c. What conclusion is appropriate when H 0 can be rejected? 2. The manager of an automobile dealership is considering a new bonus plan designed to increase sales volume. Currently, the mean sales volume is 14 automobiles per month. The manager wants to conduct a research study to see whether the new bonus plan increases sales volume. To collect data on the plan, a sample of sales personnel will be allowed to sell under the new bonus plan for a one-month period. a. Develop the null and alternative hypotheses most appropriate for this research situation. b. Comment on the conclusion when H 0 cannot be rejected. c. Comment on the conclusion when H 0 can be rejected. 3. A production line operation is designed to fill cartons with laundry detergent to a mean weight of 32 ounces. A sample of cartons is periodically selected and weighed to determine whether underfilling or overfilling is occurring. If the sample data lead to a conclusion of underfilling or overfilling, the production line will be shut down and adjusted to obtain proper filling. a. Formulate the null and alternative hypotheses that will help in deciding whether to shut down and adjust the production line. b. Comment on the conclusion and the decision when H 0 cannot be rejected. c. Comment on the conclusion and the decision when H 0 can be rejected. 4. Because of high production-changeover time and costs, a director of manufacturing must convince management that a proposed manufacturing method reduces costs before the new method can be implemented. The current production method operates with a mean cost of $220 per hour. A research study will measure the cost of the new method over a sample production period. a. Develop the null and alternative hypotheses most appropriate for this study. b. Comment on the conclusion when H 0 cannot be rejected. c. Comment on the conclusion when H 0 can be rejected. 9.2 Type I and Type II Errors The null and alternative hypotheses are competing statements about the population. Either the null hypothesis H 0 is true or the alternative hypothesis H a is true, but not both. Ideally the hypothesis testing procedure should lead to the acceptance of H 0 when H 0 is true and the
6 Chapter 9 Hypothesis Tests 341 TABLE 9.1 ERRORS AND CORRECT CONCLUSIONS IN HYPOTHESIS TESTING Population Condition H 0 True H a True Conclusion Correct Type II Accept H 0 Conclusion Error Type I Correct Reject H 0 Error Conclusion rejection of H 0 when H a is true. Unfortunately, the correct conclusions are not always possible. Because hypothesis tests are based on sample information, we must allow for the possibility of errors. Table 9.1 illustrates the two kinds of errors that can be made in hypothesis testing. The first row of Table 9.1 shows what can happen if the conclusion is to accept H 0. If H 0 is true, this conclusion is correct. However, if H a is true, we make a Type II error; that is, we accept H 0 when it is false. The second row of Table 9.1 shows what can happen if the conclusion is to reject H 0. If H 0 is true, we make a Type I error; that is, we reject H 0 when it is true. However, if H a is true, rejecting H 0 is correct. Recall the hypothesis testing illustration discussed in Section 9.1 in which an automobile product research group developed a new fuel injection system designed to increase the miles-per-gallon rating of a particular automobile. With the current model obtaining an average of 24 miles per gallon, the hypothesis test was formulated as follows. H 0 : µ 24 H a : µ 24 The alternative hypothesis, H a : µ 24, indicates that the researchers are looking for sample evidence to support the conclusion that the population mean miles per gallon with the new fuel injection system is greater than 24. In this application, the Type I error of rejecting H 0 when it is true corresponds to the researchers claiming that the new system improves the miles-per-gallon rating ( µ 24) when in fact the new system is not any better than the current system. In contrast, the Type II error of accepting H 0 when it is false corresponds to the researchers concluding that the new system is not any better than the current system ( µ 24) when in fact the new system improves miles-per-gallon performance. For the miles-per-gallon rating hypothesis test, the null hypothesis is H 0 : µ 24. Suppose the null hypothesis is true as an equality; that is, µ 24. The probability of making a Type I error when the null hypothesis is true as an equality is called the level of significance. Thus, for the miles-per-gallon rating hypothesis test, the level of significance is the probability of rejecting H 0 : µ 24 when µ 24. Because of the importance of this concept, we now restate the definition of level of significance. LEVEL OF SIGNIFICANCE The level of significance is the probability of making a Type I error when the null hypothesis is true as an equality.
7 342 STATISTICS FOR BUSINESS AND ECONOMICS If the sample data are consistent with the null hypothesis H 0, we will follow the practice of concluding do not reject H 0. This conclusion is preferred over accept H 0, because the conclusion to accept H 0 puts us at risk of making a Type II error. The Greek symbol α (alpha) is used to denote the level of significance, and common choices for α are.05 and.01. In practice, the person conducting the hypothesis test specifies the level of significance. By selecting α, that person is controlling the probability of making a Type I error. If the cost of making a Type I error is high, small values of α are preferred. If the cost of making a Type I error is not too high, larger values of α are typically used. Applications of hypothesis testing that only control for the Type I error are often called significance tests. Most applications of hypothesis testing are of this type. Although most applications of hypothesis testing control for the probability of making a Type I error, they do not always control for the probability of making a Type II error. Hence, if we decide to accept H 0, we cannot determine how confident we can be with that decision. Because of the uncertainty associated with making a Type II error, statisticians often recommend that we use the statement do not reject H 0 instead of accept H 0. Using the statement do not reject H 0 carries the recommendation to withhold both judgment and action. In effect, by not directly accepting H 0, the statistician avoids the risk of making a Type II error. Whenever the probability of making a Type II error has not been determined and controlled, we will not make the statement accept H 0. In such cases, only two conclusions are possible: do not reject H 0 or reject H 0. Although controlling for a Type II error in hypothesis testing is not common, it can be done. In Sections 9.7 and 9.8 we will illustrate procedures for determining and controlling the probability of making a Type II error. If proper controls have been established for this error, action based on the accept H 0 conclusion can be appropriate. Exercises SELF test 5. Americans spend an average of 8.6 minutes per day reading newspapers (USA Today, April 10, 1995). A researcher believes that individuals in management positions spend more than the national average time per day reading newspapers. A sample of individuals in management positions will be selected by the researcher. Data on newspaper-reading times will be used to test the following null and alternative hypotheses. H 0 : µ 8.6 H a : µ 8.6 a. What is the Type I error in this situation? What are the consequences of making this error? b. What is the Type II error in this situation? What are the consequences of making this error? 6. The label on a 3-quart container of orange juice claims that the orange juice contains an average of 1 gram of fat or less. Answer the following questions for a hypothesis test that could be used to test the claim on the label. a. Develop the appropriate null and alternative hypotheses. b. What is the Type I error in this situation? What are the consequences of making this error? c. What is the Type II error in this situation? What are the consequences of making this error? 7. Carpetland salespersons average $8000 per week in sales. Steve Contois, the firm s vice president, proposes a compensation plan with new selling incentives. Steve hopes that the results of a trial selling period will enable him to conclude that the compensation plan increases the average sales per salesperson. a. Develop the appropriate null and alternative hypotheses. b. What is the Type I error in this situation? What are the consequences of making this error? c. What is the Type II error in this situation? What are the consequences of making this error?
8 Chapter 9 Hypothesis Tests Suppose a new production method will be implemented if a hypothesis test supports the conclusion that the new method reduces the mean operating cost per hour. a. State the appropriate null and alternative hypotheses if the mean cost for the current production method is $220 per hour. b. What is the Type I error in this situation? What are the consequences of making this error? c. What is the Type II error in this situation? What are the consequences of making this error? 9.3 Population Mean: σ Known In Chapter 8 we said that the σ known case corresponds to applications in which historical data and/or other information is available that enable us to obtain a good estimate of the population standard deviation prior to sampling. In such cases the population standard deviation can, for all practical purposes, be considered known. In this section we show how to conduct a hypothesis test about a population mean for the σ known case. The methods presented in this section are exact if the sample is selected from a population that is normally distributed. In cases where it is not reasonable to assume the population is normally distributed, these methods are still applicable if the sample size is large enough. We provide some practical advice concerning the population distribution and the sample size at the end of this section. One-Tailed Test One-tailed tests about a population mean take one of the following two forms. Lower Tail Test H 0 : H a : µ µ 0 µ µ 0 Upper Tail Test H 0 : H a : µ µ 0 µ µ 0 Let us consider an example involving a lower tail test. The Federal Trade Commission (FTC) periodically conducts statistical studies designed to test the claims that manufacturers make about their products. For example, the label on a large can of Hilltop Coffee states that the can contains 3 pounds of coffee. The FTC knows that Hilltop s production process cannot place exactly 3 pounds of coffee in each can, even if the mean filling weight for the population of all cans filled is 3 pounds per can. However, as long as the population mean filling weight is at least 3 pounds per can, the rights of consumers will be protected. Thus, the FTC interprets the label information on a large can of coffee as a claim by Hilltop that the population mean filling weight is at least 3 pounds per can. We will show how the FTC can check Hilltop s claim by conducting a lower tail hypothesis test. The first step is to develop the null and alternative hypotheses for the test. If the population mean filling weight is at least 3 pounds per can, Hilltop s claim is correct. This establishes the null hypothesis for the test. However, if the population mean weight is less than 3 pounds per can, Hilltop s claim is incorrect. This establishes the alternative hypothesis. With µ denoting the population mean filling weight, the null and alternative hypotheses are as follows: H 0 : µ 3 H a : µ 3 Note that the hypothesized value of the population mean is µ 0 3.
9 344 STATISTICS FOR BUSINESS AND ECONOMICS If the sample data indicate that H 0 cannot be rejected, the statistical evidence does not support the conclusion that a label violation has occurred. Hence, no action should be taken against Hilltop. However, if the sample data indicate H 0 can be rejected, we will conclude that the alternative hypothesis, H a : µ 3, is true. In this case a conclusion of underfilling and a charge of a label violation against Hilltop would be justified. Suppose a sample of 36 cans of coffee is selected and the sample mean x is computed as an estimate of the population mean µ. If the value of the sample mean x is less than 3 pounds, the sample results will cast doubt on the null hypothesis. What we want to know is how much less than 3 pounds must x be before we would be willing to declare the difference significant and risk making a Type I error by falsely accusing Hilltop of a label violation. A key factor in addressing this issue is the value the decision maker selects for the level of significance. As noted in the preceding section, the level of significance, denoted by α, is the probability of making a Type I error by rejecting H 0 when the null hypothesis is true as an equality. The decision maker must specify the level of significance. If the cost of making a Type I error is high, a small value should be chosen for the level of significance. If the cost is not high, a larger value is more appropriate. In the Hilltop Coffee study, the director of the FTC s testing program made the following statement: If the company is meeting its weight specifications at µ 3, I would like a 99% chance of not taking any action against the company. Although I do not want to accuse the company wrongly of underfilling its product, I am willing to risk a 1% chance of making such an error. From the director s statement, we set the level of significance for the hypothesis test at α.01. Thus, we must design the hypothesis test so that the probability of making a Type I error when µ 3 is.01. For the Hilltop Coffee study, by developing the null and alternative hypotheses and specifying the level of significance for the test, we carry out the first two steps required in conducting every hypothesis test. We are now ready to perform the third step of hypothesis testing: collect the sample data and compute the value of what is called a test statistic. Test statistic For the Hilltop Coffee study, previous FTC tests show that the population standard deviation can be assumed known with a value of σ.18. In addition, these tests also show that the population of filling weights can be assumed to have a normal distribution. From the study of sampling distributions in Chapter 7 we know that if the population from which we are sampling is normally distributed, the sampling distribution of x will also be normally distributed. Thus, for the Hilltop Coffee study, the sampling distribution of x is normally distributed. With a known value of σ.18 and a sample size of n 36, Figure 9.1 shows the sampling distribution of x when the null hypothesis is true FIGURE 9.1 SAMPLING DISTRIBUTION OF x FOR THE HILLTOP COFFEE STUDY WHEN THE NULL HYPOTHESIS IS TRUE AS AN EQUALITY ( µ 0 3) Sampling distribution of x σ x = σ n =.18 = µ = 3 0 x
10 Chapter 9 Hypothesis Tests 345 The standard error of x is the standard deviation of the sampling distribution of x. as an equality; that is, when µ µ 0 3.* Note that the standard error of x is given by σ x σ n Because the sampling distribution of x is normally distributed, the sampling distribution of z x µ 0 x 3 σ x.03 is a standard normal distribution. A value of z 1 means that the value of x is one standard error below the mean, a value of z 2 means that the value of x is two standard errors below the mean, and so on. We can use the standard normal distribution table to find the lower tail probability corresponding to any z value. For instance, the standard normal table shows that the area between the mean and z 3.00 is Hence, the probability of obtaining a value of z that is three or more standard errors below the mean is As a result, the probability of obtaining a value of x that is 3 or more standard errors below the hypothesized population mean µ 0 3 is also Such a result is unlikely if the null hypothesis is true. For hypothesis tests about a population mean for the σ known case, we use the standard normal random variable z as a test statistic to determine whether x deviates from the hypothesized value µ 0 enough to justify rejecting the null hypothesis. With σ x σ n, the test statistic used in the σ known case is as follows. TEST STATISTIC FOR HYPOTHESIS TESTS ABOUT A POPULATION MEAN: σ KNOWN z x µ 0 σ n (9.1) The key question for a lower tail test is: How small must the test statistic z be before we choose to reject the null hypothesis? Two approaches can be used to answer this question. The first approach uses the value of the test statistic z to compute a probability called a p-value. The p-value measures the support (or lack of support) provided by the sample for the null hypothesis, and is the basis for determining whether the null hypothesis should be rejected given the level of significance. The second approach requires that we first determine a value for the test statistic called the critical value. For a lower tail test, the critical value serves as a benchmark for determining whether the value of the test statistic is small enough to reject the null hypothesis. We begin with the p-value approach. p-value approach In practice, the p-value approach has become the preferred method of determining whether the null hypothesis can be rejected, especially when using computer software packages such as Minitab and Excel. To begin our discussion of the use of p-values in hypothesis testing, we now provide a formal definition for a p-value. p-value The p-value is a probability, computed using the test statistic, that measures the support (or lack of support) provided by the sample for the null hypothesis. *In constructing sampling distributions for hypothesis tests, it is assumed that H 0 is satisfied as an equality.
11 346 STATISTICS FOR BUSINESS AND ECONOMICS CD file Coffee Because a p-value is a probability, it ranges from 0 to 1. In general, the smaller the p-value, the less support it indicates for the null hypothesis. A small p-value indicates a sample result that is unusual given the assumption that H 0 is true. Small p-values lead to rejection of H 0, whereas large p-values indicate the null hypothesis should not be rejected. Two steps are required to use the p-value approach. First, we must use the value of the test statistic to compute the p-value. The method used to compute a p-value depends on whether the test is lower tail, upper tail, or a two-tailed test. For a lower tail test, the p-value is the probability of obtaining a value for the test statistic at least as small as that provided by the sample. Thus, to compute the p-value for the lower tail test in the σ known case, we must find the area under the standard normal curve to the left of the test statistic. After computing the p-value, we must then decide whether it is small enough to reject the null hypothesis; as we will show, this involves comparing it to the level of significance. Let us now illustrate the p-value approach by computing the p-value for the Hilltop Coffee lower tail test. Suppose the sample of 36 Hilltop coffee cans provides a sample mean of x 2.92 pounds. Is x 2.92 small enough to cause us to reject H 0? Because this test is a lower tail test, the p-value is the area under the standard normal curve to the left of the test statistic. Using x 2.92, σ.18, and n 36, we compute the value of the test statistic z. z x µ 0 σ n Thus, the p-value is the probability that the test statistic z is less than or equal to 2.67 (the area under the standard normal curve to the left of the test statistic). Using the standard normal distribution table, we find that the area between the mean and z 2.67 is Thus, the p-value is Figure 9.2 shows that x 2.92 corresponds to z 2.67 and a p-value This p-value indicates a small probability of obtaining a sample mean of x 2.92 or smaller when sampling from a population with µ 3. This p-value does not provide much support for the null hypothesis, but is it small enough to cause us to reject H 0? The answer depends upon the level of significance for the test. As noted previously, the director of the FTC s testing program selected a value of.01 for the level of significance. The selection of α.01 means that the director is willing to accept a probability of.01 of rejecting the null hypothesis when it is true as an equality ( µ 0 3). The sample of 36 coffee cans in the Hilltop Coffee study resulted in a p-value.0038, which means that the probability of obtaining a value of x 2.92 or less when the null hypothesis is true as an equality is Because.0038 is less than or equal to α.01, we reject H 0. Therefore, we find sufficient statistical evidence to reject the null hypothesis at the.01 level of significance. We can now state the general rule for determining whether the null hypothesis can be rejected when using the p-value approach. For a level of significance α, the rejection rule using the p-value approach is as follows: REJECTION RULE USING p-value Reject H 0 if p-value α In the Hilltop Coffee test, the p-value of.0038 resulted in the rejection of the null hypothesis. Although the basis for making the rejection decision involves a comparison of the p-value to the level of significance specified by the FTC director, the observed p-value of
12 Chapter 9 Hypothesis Tests 347 FIGURE 9.2 p-value FOR THE HILLTOP COFFEE STUDY WHEN x 2.92 AND z 2.67 Sampling distribution of x σ x = σ n =.03 x = 2.92 µ = 3 0 x Sampling distribution of z = x 3.03 p-value =.0038 z = z.0038 means that we would reject H 0 for any value α For this reason, the p-value is also called the observed level of significance. Different decision makers may express different opinions concerning the cost of making a Type I error and may choose a different level of significance. By providing the p-value as part of the hypothesis testing results, another decision maker can compare the reported p-value to his or her own level of significance and possibly make a different decision with respect to rejecting H 0. Critical value approach For a lower tail test, the critical value is the value of the test statistic that corresponds to an area of α (the level of significance) in the lower tail of the sampling distribution of the test statistic. In other words, the critical value is the largest value of the test statistic that will result in the rejection of the null hypothesis. Let us return to the Hilltop Coffee example and see how this approach works. In the σ known case, the sampling distribution for the test statistic z is a standard normal distribution. Therefore, the critical value is the value of the test statistic that corresponds to an area of α.01 in the lower tail of a standard normal distribution. Using the standard normal distribution table, we find that z 2.33 provides an area of.01 in the lower tail (see Figure 9.3). Thus, if the sample results in a value of the test statistic that is less than or equal to 2.33, the corresponding p-value will be less than or equal to.01; in this case, we should reject the null hypothesis. Hence, for the Hilltop Coffee study the critical value rejection rule for a level of significance of.01 is Reject H 0 if z 2.33
13 348 STATISTICS FOR BUSINESS AND ECONOMICS FIGURE 9.3 CRITICAL VALUE FOR THE HILLTOP COFFEE HYPOTHESIS TEST Sampling distribution of z = x µ 0 σ / n α =.01 z = z In the Hilltop Coffee example, x 2.92 and the test statistic is z Because z , we can reject H 0 and conclude that Hilltop Coffee is underfilling cans. We can generalize the rejection rule for the critical value approach to handle any level of significance. The rejection rule for a lower tail test follows. REJECTION RULE FOR A LOWER TAIL TEST: CRITICAL VALUE APPROACH Reject H 0 if z z α where z α is the critical value; that is, the z value that provides an area of α in the lower tail of the standard normal distribution. The p-value approach to hypothesis testing and the critical value approach will always lead to the same rejection decision; that is, whenever the p-value is less than or equal to α, the value of the test statistic will be less than or equal to the critical value. The advantage of the p-value approach is that the p-value tells us how significant the results are (the observed level of significance). If we use the critical value approach, we only know that the results are significant at the stated level of significance. Computer procedures for hypothesis testing provide the p-value, so it is rapidly becoming the preferred method of conducting hypothesis tests. If you do not have access to a computer, you may prefer to use the critical value approach. For some probability distributions it is easier to use statistical tables to find a critical value than to use the tables to compute the p-value. This topic is discussed further in the next section. At the beginning of this section, we said that one-tailed tests about a population mean take one of the following two forms: Lower Tail Test H 0 : H a : µ µ 0 µ µ 0 Upper Tail Test H 0 : H a : µ µ 0 µ µ 0 We used the Hilltop Coffee study to illustrate how to conduct a lower tail test. We can use the same general approach to conduct an upper tail test. The test statistic z is still computed using equation (9.1). But, for an upper tail test, the p-value is the probability of obtaining
14 Chapter 9 Hypothesis Tests 349 a value for the test statistic at least as large as that provided by the sample. Thus, to compute the p-value for the upper tail test in the σ known case, we must find the area under the standard normal curve to the right of the test statistic. Using the critical value approach causes us to reject the null hypothesis if the value of the test statistic is greater than or equal to the critical value z α ; in other words, we reject H 0 if z z α. Two-Tailed Test In hypothesis testing, the general form for a two-tailed test about a population mean is as follows: H 0 : H a : In this subsection we show how to conduct a two-tailed test about a population mean for the σ known case. As an illustration, we consider the hypothesis testing situation facing MaxFlight, Inc. The U.S. Golf Association (USGA) establishes rules that manufacturers of golf equipment must meet if their products are to be acceptable for use in USGA events. MaxFlight uses a hightechnology manufacturing process to produce golf balls with an average distance of 295 yards. Sometimes, however, the process gets out of adjustment and produces golf balls with average distances different from 295 yards. When the average distance falls below 295 yards, the company worries about losing sales because the golf balls do not provide as much distance as advertised. When the average distance passes 295 yards, MaxFlight s golf balls may be rejected by the USGA for exceeding the overall distance standard concerning carry and roll. MaxFlight s quality control program involves taking periodic samples of 50 golf balls to monitor the manufacturing process. For each sample, a hypothesis test is conducted to determine whether the process has fallen out of adjustment. Let us develop the null and alternative hypotheses. We begin by assuming that the process is functioning correctly; that is, the golf balls being produced have a mean distance of 295 yards. This assumption establishes the null hypothesis. The alternative hypothesis is that the mean distance is not equal to 295 yards. With a hypothesized value of µ 0 295, the null and alternative hypotheses for the MaxFlight hypothesis test are as follows: H 0 : H a : µ µ 0 µ µ 0 µ 295 µ 295 If the sample mean x is significantly less than 295 yards or significantly greater than 295 yards, we will reject H 0. In this case, corrective action will be taken to adjust the manufacturing process. On the other hand, if x does not deviate from the hypothesized mean µ by a significant amount, H 0 will not be rejected and no action will be taken to adjust the manufacturing process. The quality control team selected α.05 as the level of significance for the test. Data from previous tests conducted when the process was known to be in adjustment show that the population standard deviation can be assumed known with a value of σ 12. Thus, with a sample size of n 50, the standard error of x is σ x σ n Because the sample size is large, the central limit theorem (see Chapter 7) allows us to conclude that the sampling distribution of x can be approximated by a normal distribution.
15 350 STATISTICS FOR BUSINESS AND ECONOMICS CD file GolfTest Figure 9.4 shows the sampling distribution of x for the MaxFlight hypothesis test with a hypothesized population mean of µ Suppose that a sample of 50 golf balls is selected and that the sample mean is x yards. This sample mean provides support for the conclusion that the population mean is larger than 295 yards. Is this value of x enough larger than 295 to cause us to reject H 0 at the.05 level of significance? In the previous section we described two approaches that can be used to answer this question: the p-value approach and the critical value approach. p-value approach Recall that the p-value is a probability, computed using the test statistic, that measures the support (or lack of support) provided by the sample for the null hypothesis. For a two-tailed test, values of the test statistic in either tail show a lack of support for the null hypothesis. For a two-tailed test, the p-value is the probability of obtaining a value for the test statistic at least as unlikely as that provided by the sample. Let us see how the p-value is computed for the MaxFlight hypothesis test. First we compute the value of the test statistic. For the σ known case, the test statistic z is a standard normal random variable. Using Equation (9.1) with x 297.6, the value of the test statistic is z x µ 0 σ n Now to compute the p-value we must find the probability of obtaining a value for the test statistic at least as unlikely as z Clearly values of z 1.53 are at least as unlikely. But, because this is a two-tailed test, values of z 1.53 are also at least as unlikely as the value of the test statistic provided by the sample. Referring to Figure 9.5, we see that the two-tailed p-value in this case is given by P(z 1.53) P(z 1.53). Because the normal curve is symmetric, we can compute this probability by finding the area under the standard normal curve to the right of z 1.53 and doubling it. The table for the standard normal distribution shows that the area between the mean and z 1.53 is Thus, the area under the standard normal curve to the right of the test statistic z 1.53 is Doubling this, we find the p-value for the MaxFlight two-tailed hypothesis test is p-value 2(.0630) Next we compare the p-value to the level of significance to see if the null hypothesis should be rejected. With a level of significance of α.05, we do not reject H 0 because the p-value Because the null hypothesis is not rejected, no action will be taken to adjust the MaxFlight manufacturing process. FIGURE 9.4 SAMPLING DISTRIBUTION OF x FOR THE MAXFLIGHT HYPOTHESIS TEST Sampling distribution of x σ x = σ n = 12 = µ 0 = 295 x
16 Chapter 9 Hypothesis Tests 351 FIGURE 9.5 p-value FOR THE MAXFLIGHT HYPOTHESIS TEST P(z 1.53) =.0630 P(z 1.53) = z p-value = 2(.0630) =.1260 The computation of the p-value for a two-tailed test may seem a bit confusing as compared to the computation of the p-value for a one-tailed test. But, it can be simplified by following these three steps. 1. Compute the value of the test statistic z. 2. If the value of the test statistic is in the upper tail (z 0), find the area under the standard normal curve to the right of z. If the value of the test statistic is in the lower tail, find the area under the standard normal curve to the left of z. 3. Double the tail area, or probability, obtained in step 2 to obtain the p-value. In practice, the computation of the p-value is done automatically when using computer software such as Minitab and Excel. For instance, Figure 9.6 shows the Minitab output for the MaxFlight hypothesis test. The sample mean x 297.6, the test statistic z 1.53, and the p-value.126 are shown. The step-by-step procedure used to obtain the Minitab output is described in Appendix 9.1. Critical value approach Before leaving this section, let us see how the test statistic z can be compared to a critical value to make the hypothesis testing decision for a two-tailed test. Figure 9.7 shows that the critical values for the test will occur in both the lower and upper tails of the standard normal distribution. With a level of significance of α.05, the area in each tail beyond the critical values is α/2.05/ Using the table of areas for the standard normal distribution, we find the critical values for the test statistic are FIGURE 9.6 MINITAB OUTPUT FOR THE MAXFLIGHT HYPOTHESIS TEST Test of mu = 295 vs not = 295 The assumed sigma = 12 Variable N Mean StDev SE Mean Yards Variable 95.0% CI Yards ( , ) Z 1.53 P 0.126
17 352 STATISTICS FOR BUSINESS AND ECONOMICS FIGURE 9.7 CRITICAL VALUES FOR THE MAXFLIGHT HYPOTHESIS TEST Area =.025 Area = z Reject H 0 Reject H 0 z and z Thus, using the critical value approach, the two-tailed rejection rule is Reject H 0 if z 1.96 or if z 1.96 Because the value of the test statistic for the MaxFlight study is z 1.53, the statistical evidence will not permit us to reject the null hypothesis at the.05 level of significance. Summary and Practical Advice We presented examples of a lower tail test and a two-tailed test about a population mean. Based upon these examples, we can now summarize the hypothesis testing procedures about a population mean for the σ known case as shown in Table 9.2. Note that µ 0 is the hypothesized value of the population mean. The hypothesis testing steps followed in the two examples presented in this section are common to every hypothesis test. STEPS OF HYPOTHESIS TESTING Step 1. Develop the null and alternative hypotheses. Step 2. Specify the level of significance. Step 3. Collect the sample data and compute the value of the test statistic. p-value Approach Step 4. Use the value of the test statistic to compute the p-value. Step 5. Reject H 0 if the p-value α. Critical Value Approach Step 4. Use the level of significance to determine the critical value and the rejection rule. Step 5. Use the value of the test statistic and the rejection rule to determine whether to reject H 0.
18 Chapter 9 Hypothesis Tests 353 TABLE 9.2 SUMMARY OF HYPOTHESIS TESTS ABOUT A POPULATION MEAN: σ KNOWN CASE Hypotheses Lower Tail Test Upper Tail Test Two-Tailed Test H 0 : µ µ 0 H 0 : µ µ 0 H 0 : µ µ 0 H a : µ µ 0 H a : µ µ 0 H a : µ µ 0 Test Statistic z x µ 0 σ n z x µ 0 σ n z x µ 0 σ n Rejection Rule: Reject H 0 if Reject H 0 if Reject H 0 if p-value Approach p-value α p-value α p-value α Rejection Rule: Reject H 0 if Reject H 0 if Reject H 0 if Critical Value z z α z z α z z α/2 Approach or if z z α/2 Practical advice about the sample size for hypothesis tests is similar to the advice we provided about the sample size for interval estimation in Chapter 8. In most applications, a sample size of n 30 is adequate when using the hypothesis testing procedure described in this section. In cases where the sample size is less than 30, the distribution of the population from which we are sampling becomes an important consideration. If the population is normally distributed, the hypothesis testing procedure that we described is exact and can be used for any sample size. If the population is not normally distributed but is at least roughly symmetric, sample sizes as small as 15 can be expected to provide acceptable results. With smaller sample sizes, the hypothesis testing procedure presented in this section should only be used if the analyst believes, or is willing to assume, that the population is at least approximately normally distributed. Relationship Between Interval Estimation and Hypothesis Testing We close this section by discussing the relationship between interval estimation and hypothesis testing. In Chapter 8 we showed how to develop a confidence interval estimate of a population mean. For the σ known case, the confidence interval estimate of a population mean corresponding to a 1 α confidence coefficient is given by x z α/2 σ n (9.2) Conducting a hypothesis test requires us first to develop the hypotheses about the value of a population parameter. In the case of the population mean, the two-tailed test takes the form H 0 : µ µ 0 H a : µ µ 0 where µ 0 is the hypothesized value for the population mean. Using the two-tailed critical value approach, we do not reject H 0 for values of the sample mean x that are within z α/2 and z α/2 standard errors of µ 0. Thus, the do-not-reject region for the sample mean x in a two-tailed hypothesis test with a level of significance of α is given by µ 0 z α/2 σ n (9.3)
19 354 STATISTICS FOR BUSINESS AND ECONOMICS A close look at expression (9.2) and expression (9.3) provides insight about the relationship between the estimation and hypothesis testing approaches to statistical inference. Note in particular that both procedures require the computation of the values z α/2 and σ n. Focusing on α, we see that a confidence coefficient of (1 α) for interval estimation corresponds to a level of significance of α in hypothesis testing. For example, a 95% confidence interval corresponds to a.05 level of significance for hypothesis testing. Furthermore, expressions (9.2) and (9.3) show that, because z α/2 ( σ n ) is the plus or minus value for both expressions, if x is in the do-not-reject region defined by (9.3), the hypothesized value µ 0 will be in the confidence interval defined by (9.2). Conversely, if the hypothesized value µ 0 is in the confidence interval defined by (9.2), the sample mean x will be in the do-not-reject region for the hypothesis H 0 : µ µ 0 as defined by (9.3). These observations lead to the following procedure for using a confidence interval to conduct a two-tailed hypothesis test. A CONFIDENCE INTERVAL APPROACH TO TESTING A HYPOTHESIS OF THE FORM H 0 : µ µ 0 H a : µ µ 0 1. Select a simple random sample from the population and use the value of the sample mean x to develop the confidence interval for the population mean µ. For a two-tailed hypothesis test, the null hypothesis can be rejected if the confidence interval does not include µ 0. x z α/2 σ n 2. If the confidence interval contains the hypothesized value µ 0, do not reject H 0. Otherwise, reject H 0. Let us return to the MaxFlight hypothesis test, which resulted in the following twotailed test. To test this hypothesis with a level of significance of α.05, we sampled 50 golf balls and found a sample mean distance of x yards. Recall that the population standard deviation is σ 12. Using these results with z , we find that the 95% confidence interval estimate of the population mean is or H 0 : µ 295 H a : µ 295 x z.025 σ n to This finding enables the quality control manager to conclude with 95% confidence that the mean distance for the population of golf balls is between and yards. Because the hypothesized value for the population mean, µ 0 295, is in this interval, the hypothesis testing conclusion is that the null hypothesis, H 0 : µ 295, cannot be rejected.
20 Chapter 9 Hypothesis Tests 355 Note that this discussion and example pertain to two-tailed hypothesis tests about a population mean. However, the same confidence interval and two-tailed hypothesis testing relationship exists for other population parameters. The relationship can also be extended to one-tailed tests about population parameters. Doing so, however, requires the development of one-sided confidence intervals. NOTES AND COMMENTS 1. In Appendix 9.2 we show how to compute p-values using Excel. The smaller the p-value the greater the evidence against H 0 and the more the evidence in favor of H a. Here are some guidelines statisticians suggest for interpreting small p-values. Less than.01 Overwhelming evidence to conclude H a is true. Between.01 and.05 Strong evidence to conclude H a is true. Between.05 and.10 Weak evidence to conclude H a is true. Greater than.10 Insufficient evidence to conclude H a is true. Exercises Note to Student: Some of the exercises that follow ask you to use the p-value approach and others ask you to use the critical value approach. Both methods will provide the same hypothesis testing conclusion. We provide exercises with both methods to give you practice using both. In later sections and in following chapters, we will generally emphasize the p-value approach as the preferred method, but you may select either based on personal preference. Methods 9. Consider the following hypothesis test: H 0 : µ 20 H a : µ 20 SELF SELF test test A sample of 50 provided a sample mean of The population standard deviation is 2. a. Compute the value of the test statistic. b. What is the p-value? c. Using α.05, what is your conclusion? d. What is the rejection rule using the critical value? What is your conclusion? 10. Consider the following hypothesis test: H 0 : µ 25 H a : µ 25 A sample of 40 provided a sample mean of The population standard deviation is 6. a. Compute the value of the test statistic. b. What is the p-value? c. At α.01, what is your conclusion? d. What is the rejection rule using the critical value? What is your conclusion? 11. Consider the following hypothesis test: H 0 : µ 15 H a : µ 15 A sample of 50 provided a sample mean of The population standard deviation is 3.
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Practice problems for Homework 1 - confidence intervals and hypothesis testing. Read sections 10..3 and 10.3 of the text. Solve the practice problems below. Open the Homework Assignment 1 and solve the
8-2 Basics of Hypothesis Testing Definitions This section presents individual components of a hypothesis test. We should know and understand the following: How to identify the null hypothesis and alternative
A Trial Analogy for Statistical Slide 1 Hypothesis Testing Legal Trial Begin with claim: Smith is not guilty If this is rejected, we accept Smith is guilty reasonable doubt Present evidence (facts) Evaluate
Hypothesis tests, confidence intervals, and bootstrapping Business Statistics 41000 Fall 2015 1 Topics 1. Hypothesis tests Testing a mean: H0 : µ = µ 0 Testing a proportion: H0 : p = p 0 Testing a difference
Hypothesis Testing --- One Mean A hypothesis is simply a statement that something is true. Typically, there are two hypotheses in a hypothesis test: the null, and the alternative. Null Hypothesis The hypothesis
University of California, Los Angeles Department of Statistics Statistics 13 Elements of a hypothesis test: Hypothesis testing Instructor: Nicolas Christou 1. Null hypothesis, H 0 (always =). 2. Alternative
MBA/MIB 5315 Sample Test Problems Page 1 of 1 1. An English survey of 3000 medical records showed that smokers are more inclined to get depressed than non-smokers. Does this imply that smoking causes depression?
Chapter 9 Introduction to Hypothesis Testing 9.2 - Hypothesis Testing Hypothesis testing is an eample of inferential statistics We use sample information to draw conclusions about the population from which
Basic Statistics Hypothesis Testing Hypothesis Testing Learning Intentions Today we will understand: Formulating the null and alternative hypothesis Distinguish between a one-tail and two-tail hypothesis