Solve the problem. Use the critical-value approach.
A sheet-metal press stamps out bolt washers with a nominal inner diameter of 0.25 inches. Measurement of the inner diameters of a random sample of 14 washers produced the following results (in inches):
![](data:image/png;base64, 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)
The normal scores of the data are summarized below:
![](data:image/png;base64, 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Using technology, perform the following hypothesis test: at the 1% significance level, determine whether the mean washer diameter for this machine exceeds the nominal value. Comment on the appropriateness of the test.
◦ Test statistic: t = 4.5865; Critical value: 2.650.
Since the test statistic is greater than the critical value, reject the null hypothesis H
0: μ = 0.25 inches.
There is sufficient evidence to conclude that the inner diameter is larger than the nominal value. However, the normal probability plot indicates that the data are not distributed normally, so the t-test may not be appropriate.
◦ Test statistic: t = 2.6503; Critical value: 4.5865.
Since the test statistic is less than the critical value, do not reject the null hypothesis
![](data:image/png;base64, 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)
. There is insufficient evidence to conclude that the inner diameter is larger than the nominal value. The normal probability distribution plot indicates that the t-test is an appropriate test.
◦ Test statistic: t = 4.5865; Critical value: 1.7709.
Since the test statistic is greater than the critical value, reject the null hypothesis H
0: μ = 0.25 inches.
There is sufficient evidence to conclude that the inner diameter is larger than the nominal value. However, the normal probability plot indicates that the data are not distributed normally, so the t-test may not be appropriate.
◦ Test statistic: t = 2.6503; Critical value: 4.5865.
Since the test statistic is less than the critical value, do not reject the null hypothesis
![](data:image/png;base64, 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)
. There is insufficient evidence to conclude that the inner diameter is larger than the nominal value. However, the normal probability plot indicates that the data are not distributed normally, so the t-test may not be appropriate.