Download presentation

Presentation is loading. Please wait.

Published byAva Sturmer Modified over 4 years ago

1
1 1 Slide © 2008 Thomson South-Western. All Rights Reserved Chapter 9 Hypothesis Testing Developing Null and Alternative Hypotheses Developing Null and Alternative Hypotheses Type I and Type II Errors Type I and Type II Errors Population Mean: Known Population Mean: Known Population Mean: Unknown Population Mean: Unknown

2
2 2 Slide © 2008 Thomson South-Western. All Rights Reserved Developing Null and Alternative Hypotheses Hypothesis testing can be used to determine whether Hypothesis testing can be used to determine whether a statement about the value of a population parameter a statement about the value of a population parameter should or should not be rejected. should or should not be rejected. The null hypothesis, denoted by H 0, is a tentative The null hypothesis, denoted by H 0, is a tentative assumption about a population parameter. assumption about a population parameter. The alternative hypothesis, denoted by H a, is the The alternative hypothesis, denoted by H a, is the opposite of what is stated in the null hypothesis. opposite of what is stated in the null hypothesis. The alternative hypothesis is what the test is The alternative hypothesis is what the test is attempting to establish. attempting to establish.

3
3 3 Slide © 2008 Thomson South-Western. All Rights Reserved n Testing Research Hypotheses Developing Null and Alternative Hypotheses The research hypothesis should be expressed as The research hypothesis should be expressed as the alternative hypothesis. the alternative hypothesis. The conclusion that the research hypothesis is true The conclusion that the research hypothesis is true comes from sample data that contradict the null comes from sample data that contradict the null hypothesis. hypothesis.

4
4 4 Slide © 2008 Thomson South-Western. All Rights Reserved Developing Null and Alternative Hypotheses n Testing the Validity of a Claim Manufacturers’ claims are usually given the benefit Manufacturers’ claims are usually given the benefit of the doubt and stated as the null hypothesis. of the doubt and stated as the null hypothesis. The conclusion that the claim is false comes from The conclusion that the claim is false comes from sample data that contradict the null hypothesis. sample data that contradict the null hypothesis.

5
5 5 Slide © 2008 Thomson South-Western. All Rights Reserved n Testing in Decision-Making Situations Developing Null and Alternative Hypotheses A decision maker might have to choose between A decision maker might have to choose between two courses of action, one associated with the null two courses of action, one associated with the null hypothesis and another associated with the hypothesis and another associated with the alternative hypothesis. alternative hypothesis. Example: Accepting a shipment of goods from a Example: Accepting a shipment of goods from a supplier or returning the shipment of goods to the supplier or returning the shipment of goods to the supplier supplier

6
6 6 Slide © 2008 Thomson South-Western. All Rights Reserved One-tailed(lower-tail)One-tailed(upper-tail)Two-tailed Summary of Forms for Null and Alternative Hypotheses about a Population Mean n The equality part of the hypotheses always appears in the null hypothesis. in the null hypothesis. In general, a hypothesis test about the value of a In general, a hypothesis test about the value of a population mean must take one of the following population mean must take one of the following three forms (where 0 is the hypothesized value of three forms (where 0 is the hypothesized value of the population mean). the population mean).

7
7 7 Slide © 2008 Thomson South-Western. All Rights Reserved Type I Error Because hypothesis tests are based on sample data, Because hypothesis tests are based on sample data, we must allow for the possibility of errors. we must allow for the possibility of errors. n A Type I error is rejecting H 0 when it is true. n The probability of making a Type I error when the null hypothesis is true as an equality is called the null hypothesis is true as an equality is called the level of significance. level of significance. n Applications of hypothesis testing that only control the Type I error are often called significance tests. the Type I error are often called significance tests.

8
8 8 Slide © 2008 Thomson South-Western. All Rights Reserved Type II Error n A Type II error is accepting H 0 when it is false. n It is difficult to control for the probability of making a Type II error. a Type II error. n Statisticians avoid the risk of making a Type II error by using “do not reject H 0 ” and not “accept H 0 ”. error by using “do not reject H 0 ” and not “accept H 0 ”.

9
9 9 Slide © 2008 Thomson South-Western. All Rights Reserved p -Value Approach to One-Tailed Hypothesis Testing A p -value is a probability that provides a measure A p -value is a probability that provides a measure of the evidence against the null hypothesis of the evidence against the null hypothesis provided by the sample. provided by the sample. The smaller the p -value, the more evidence there The smaller the p -value, the more evidence there is against H 0. is against H 0. A small p -value indicates the value of the test A small p -value indicates the value of the test statistic is unusual given the assumption that H 0 statistic is unusual given the assumption that H 0 is true. is true. The p -value is used to determine if the null The p -value is used to determine if the null hypothesis should be rejected. hypothesis should be rejected.

10
10 Slide © 2008 Thomson South-Western. All Rights Reserved Critical Value Approach to One-Tailed Hypothesis Testing The test statistic z has a standard normal probability The test statistic z has a standard normal probability distribution. distribution. We can use the standard normal probability We can use the standard normal probability distribution table to find the z -value with an area distribution table to find the z -value with an area of in the lower (or upper) tail of the distribution. of in the lower (or upper) tail of the distribution. The value of the test statistic that established the The value of the test statistic that established the boundary of the rejection region is called the boundary of the rejection region is called the critical value for the test. critical value for the test. n The rejection rule is: Lower tail: Reject H 0 if z < - z Lower tail: Reject H 0 if z < - z Upper tail: Reject H 0 if z > z Upper tail: Reject H 0 if z > z

11
11 Slide © 2008 Thomson South-Western. All Rights Reserved 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 test statistic. p -Value Approach Step 4. Use the value of the test statistic to compute the p -value. p -value. Step 5. Reject H 0 if p -value < .

12
12 Slide © 2008 Thomson South-Western. All Rights Reserved 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. rule to determine whether to reject H 0. Steps of Hypothesis Testing

13
13 Slide © 2008 Thomson South-Western. All Rights Reserved p -Value Approach to Two-Tailed Hypothesis Testing The rejection rule: The rejection rule: Reject H 0 if the p -value < . Reject H 0 if the p -value < . Compute the p -value using the following three steps: Compute the p -value using the following three steps: 3. Double the tail area obtained in step 2 to obtain the p –value. the p –value. 2. If z is in the upper tail ( z > 0), find the area under the standard normal curve to the right of z. the standard normal curve to the right of z. If z is in the lower tail ( z < 0), find the area under If z is in the lower tail ( z < 0), find the area under the standard normal curve to the left of z. the standard normal curve to the left of z. 1. Compute the value of the test statistic z.

14
14 Slide © 2008 Thomson South-Western. All Rights Reserved Critical Value Approach to Two-Tailed Hypothesis Testing The critical values will occur in both the lower and The critical values will occur in both the lower and upper tails of the standard normal curve. upper tails of the standard normal curve. n The rejection rule is: Reject H 0 if z z /2. Reject H 0 if z z /2. Use the standard normal probability distribution Use the standard normal probability distribution table to find z /2 (the z -value with an area of /2 in table to find z /2 (the z -value with an area of /2 in the upper tail of the distribution). the upper tail of the distribution).

15
15 Slide © 2008 Thomson South-Western. All Rights Reserved Confidence Interval Approach to Two-Tailed Tests About a Population Mean Select a simple random sample from the population Select a simple random sample from the population and use the value of the sample mean to develop and use the value of the sample mean to develop the confidence interval for the population mean . the confidence interval for the population mean . (Confidence intervals are covered in Chapter 8.) (Confidence intervals are covered in Chapter 8.) If the confidence interval contains the hypothesized If the confidence interval contains the hypothesized value 0, do not reject H 0. Otherwise, reject H 0. value 0, do not reject H 0. Otherwise, reject H 0.

16
16 Slide © 2008 Thomson South-Western. All Rights Reserved n Test Statistic Tests About a Population Mean: Unknown This test statistic has a t distribution with n - 1 degrees of freedom. with n - 1 degrees of freedom.

17
17 Slide © 2008 Thomson South-Western. All Rights Reserved n Rejection Rule: p -Value Approach H 0 : Reject H 0 if t > t Reject H 0 if t < - t Reject H 0 if t t H 0 : H 0 : Tests About a Population Mean: Unknown n Rejection Rule: Critical Value Approach Reject H 0 if p –value <

18
18 Slide © 2008 Thomson South-Western. All Rights Reserved p -Values and the t Distribution The format of the t distribution table provided in most The format of the t distribution table provided in most statistics textbooks does not have sufficient detail statistics textbooks does not have sufficient detail to determine the exact p -value for a hypothesis test. to determine the exact p -value for a hypothesis test. However, we can still use the t distribution table to However, we can still use the t distribution table to identify a range for the p -value. identify a range for the p -value. An advantage of computer software packages is that An advantage of computer software packages is that the computer output will provide the p -value for the the computer output will provide the p -value for the t distribution. t distribution.

19
19 Slide © 2008 Thomson South-Western. All Rights Reserved n The equality part of the hypotheses always appears in the null hypothesis. in the null hypothesis. In general, a hypothesis test about the value of a In general, a hypothesis test about the value of a population proportion p must take one of the population proportion p must take one of the following three forms (where p 0 is the hypothesized following three forms (where p 0 is the hypothesized value of the population proportion). value of the population proportion). A Summary of Forms for Null and Alternative Hypotheses About a Population Proportion One-tailed (lower tail) One-tailed (upper tail) Two-tailed

20
20 Slide © 2008 Thomson South-Western. All Rights Reserved n Test Statistic Tests About a Population Proportion where: assuming np > 5 and n (1 – p ) > 5

21
21 Slide © 2008 Thomson South-Western. All Rights Reserved n Rejection Rule: p –Value Approach H 0 : p p Reject H 0 if z > z Reject H 0 if z < - z Reject H 0 if z z H 0 : p p H 0 : p p Tests About a Population Proportion Reject H 0 if p –value < n Rejection Rule: Critical Value Approach

Similar presentations

© 2019 SlidePlayer.com Inc.

All rights reserved.

To make this website work, we log user data and share it with processors. To use this website, you must agree to our Privacy Policy, including cookie policy.

Ads by Google