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What is the difference between Type I and Type II errors in hypothesis testing? How do α and β relate to Type I and Type II errors?

Short Answer

Expert verified

Type I error is the rejection of the true null hypothesis, while Type II error occurs when one fails to reject the false null hypothesis. The value of Type I error is predefined and thus can be controlled, whereas Type II error can not be controlled.

Step by step solution

01

Given Information

In a hypothesis testing problem taking the right decision about the null hypothesis is very important. Otherwise, it may lead to the errors known as Type I and Type II errors.

02

Stating the difference between the two types of errors

Type I error is the rejection of the true null hypothesis, while Type II error occurs when one fails to reject the false null hypothesis. The value of Type I error is predefined and thus can be controlled, whereas Type II error can not be controlled.

A Type I error is denoted by α , and a Type II error β . A Type I error is also known as the size of the test.

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Most popular questions from this chapter

Which of the elements of a test of hypothesis can and should be specified prior to analyzing the data that are to be used to conduct the test

Feminized faces in TV commercials. Television commercials most often employ females or “feminized” males to pitch a company’s product. Research published in Nature (August 27 1998) revealed that people are, in fact, more attracted to “feminized” faces, regardless of gender. In one experiment, 50 human subjects viewed both a Japanese female face and a Caucasian male face on a computer. Using special computer graphics, each subject could morph the faces (by making them more feminine or more masculine) until they attained the “most attractive” face. The level of feminization x (measured as a percentage) was measured.

a. For the Japanese female face, x = 10.2% and s = 31.3%. The researchers used this sample information to test the null hypothesis of a mean level of feminization equal to 0%. Verify that the test statistic is equal to 2.3.

b. Refer to part a. The researchers reported the p-value of the test as p = .021. Verify and interpret this result.

c. For the Caucasian male face, x = 15.0% and s = 25.1%. The researchers reported the test statistic (for the test of the null hypothesis stated in part a) as 4.23 with an associated p-value of approximately 0. Verify and interpret these results.

Suppose a random sample of 100 observations from a binomial population gives a value of \(\hat p = .63\) and you wish to test the null hypothesis that the population parameter p is equal to .70 against the alternative hypothesis that p is less than .70.

a. Noting that\(\hat p = .63\) what does your intuition tell you? Does the value of \(\hat p\) appear to contradict the null hypothesis?

If you select a very small value for αwhen conducting a hypothesis test, will β tend to be big or small? Explain.

Stability of compounds in new drugs. Refer to the ACS Medicinal Chemistry Letters (Vol. 1, 2010) study of the metabolic stability of drugs, Exercise 2.22 (p. 83). Recall that two important values computed from the testing phase are the fraction of compound unbound to plasma (fup) and the fraction of compound unbound to microsomes (fumic). A key formula for assessing stability assumes that the fup/fumic ratio is 1:1. Pharmacologists at Pfizer Global Research and Development tested 416 drugs and reported the fup/fumic ratio for each. These data are saved in the FUP file, and summary statistics are provided in the accompanying Minitab printout. Suppose the pharmacologists want to determine if the true mean ratio, μ, differs from 1.

a. Specify the null and alternative hypotheses for this test.

b. Descriptive statistics for the sample ratios are provided in the Minitab printout on page 410. Note that the sample mean,\(\overline x = .327\)is less than 1. Consequently, a pharmacologist wants to reject the null hypothesis. What are the problems with using such a decision rule?

c. Locate values of the test statistic and corresponding p-value on the printout.

d. Select a value of\(\alpha \)the probability of a Type I error. Interpret this value in the words of the problem.

e. Give the appropriate conclusion based on the results of parts c and d.

f. What conditions must be satisfied for the test results to be valid?

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