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Interpreting Power For the sample data in Example 1 鈥淎dult Sleep鈥 from this section, Minitab and StatCrunch show that the hypothesis test has power of 0.4943 of supporting the claim that\(\mu < 7\)hours of sleep when the actual population mean is 6.0 hours of sleep. Interpret this value of the power, then identify the value of\(\beta \)and interpret that value. (For the t test in this section, a 鈥渘oncentrality parameter鈥 makes calculations of power much more complicated than the process described in Section 8-1, so software is recommended for power calculations.)

Short Answer

Expert verified

If the true population mean sleep time is equal to 6.0 hours, there is a 49.43% chance of making the correct conclusion of rejecting the false null hypothesis/supporting the stated claim.

The value of \(\beta \) is equal to 0.5057.

If the true population mean sleep time is equal to 6.0 hours, there is a 50.57% chance of making the incorrect conclusion of failing to reject the false null hypothesis/rejecting the stated claim.

Step by step solution

01

Given information

A sample of 12 adults is randomly selected. The actual population鈥檚 mean sleep time is equal to 6.0 hours. It is claimed that the mean sleep time is less than 7 hours.

02

Hypotheses

The appropriate hypotheses for testing the given claim are written as follows:

Null hypothesis: The mean sleep time is equal to 7.0 hours.

\({H_0}:\mu = 7\)

Alternative Hypothesis: The mean sleep time is less than 7.0 hours.

\({H_0}:\mu < 7\)

The test is left-tailed.

03

Power of the test

The power of the test refers to the probability of rejecting the null hypothesis when it is actually false. In simple words, the value of the power of the test explains the probability of making the correct conclusion of the claim.

Here, the value of the power of the test is equal to 0.4943.

The actual population鈥檚 mean sleep time is equal to 6.0 hours. It is claimed that the mean sleep time is less than 7 hours.

Thus, it means that there is a 49.43% probability of making the correct conclusion that the mean sleep time is less than 7 hours (rejecting the false null hypothesis) when the population鈥檚 true mean sleep time is equal to 6.0 hours.

04

Value of \(\beta \)

The value of\(\beta \)is computed using the following formula:

\(\begin{array}{c}{\rm{Power}} = 1 - \beta \\\beta = 1 - {\rm{Power}}\end{array}\)

Therefore, the value of\(\beta \)is equal to:

\(\begin{array}{c}\beta = 1 - {\rm{Power}}\\ = 1 - 0.4943\\ = 0.5057\end{array}\)

Thus, the value of \(\beta \) is equal to 0.5057.

05

Interpretation of the value of \(\beta \)

The value of\(\beta \)is the value of the Type II error which is the probability of failing to reject the null hypothesis when it is actually false.

In simple words,\(\beta \)refers to the probability of making an incorrect conclusion about the stated claim.

For the given claim, the value of\(\beta \)represents the probability of rejecting the claim that the mean sleep time is less than 7 hours when the true population鈥檚 mean sleep time is actually equal to 6.0 hours.

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Most popular questions from this chapter

In Exercises 9鈥12, refer to the exercise identified. Make subjective estimates to decide whether results are significantly low or significantly high, then state a conclusion about the original claim. For example, if the claim is that a coin favours heads and sample results consist of 11 heads in 20 flips, conclude that there is not sufficient evidence to support the claim that the coin favours heads (because it is easy to get 11 heads in 20 flips by chance with a fair coin).

Exercise 8 鈥淧ulse Rates鈥

Testing Claims About Proportions. In Exercises 9鈥32, test the given claim. Identify the null hypothesis, alternative hypothesis, test statistic, P-value, or critical value(s), then state the conclusion about the null hypothesis, as well as the final conclusion that addresses the original claim. Use the P-value method unless your instructor specifies otherwise. Use the normal distribution as an approximation to the binomial distribution, as described in Part 1 of this section.

Is Nessie Real? This question was posted on the America Online website: Do you believe the Loch Ness monster exists? Among 21,346 responses, 64% were 鈥測es.鈥 Use a 0.01 significance level to test the claim that most people believe that the Loch Ness monster exists. How is the conclusion affected by the fact that Internet users who saw the question could decide whether to respond?

Final Conclusions. In Exercises 25鈥28, use a significance level of = 0.05 and use the given information for the following:

a. State a conclusion about the null hypothesis. (Reject H0 or fail to reject H0.)

b. Without using technical terms or symbols, state a final conclusion that addresses the original claim.

Original claim: Fewer than 90% of adults have a cell phone. The hypothesis test results in a P-value of 0.0003.

Critical Values. In Exercises 21鈥24, refer to the information in the given exercise and do the following.

a. Find the critical value(s).

b. Using a significance level of = 0.05, should we reject H0or should we fail to reject H0?

Exercise 17

In Exercises 1鈥4, use these results from a USA Today survey in which 510 people chose to respond to this question that was posted on the USA Today website: 鈥淪hould Americans replace passwords with biometric security (fingerprints, etc)?鈥 Among the respondents, 53% said 鈥測es.鈥 We want to test the claim that more than half of the population believes that passwords should be replaced with biometric security.

Number and Proportion

a. Identify the actual number of respondents who answered 鈥測es.鈥

b. Identify the sample proportion and the symbol used to represent it.

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