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Question 914:



No answer provided yet.We can create a confidence interval around a sample to test a hypothesis about whether a population mean is a specific value. In this question it appears the population mean is missing, but I can assume from the data in might be 50.

To create the confidence interval we first compute the standard error of the mean (SE) by dividing the standard deviation by the square root of the sample size:

3/SQRT(64) = .375

Next we compute the margin of error by multiplying the SE times a t-critical value for a 95% level of confidence. Using a t-table or the Excel function =TINV(.05, 64-1) we get the critical value of 1.99. Multiplying this times the SE we get the margin of error of .749.

Now we add and subtract this margin of error to the sample mean and get a 95% confidence interval of 48.251 to 49.749. Because the confidence interval does not contain 50, we have good evidence that the population mean is not 50.

To run the hypothesis test we can conduct a 1-sample t-test using the same data. The test statistic is the difference between the sample mean and the SE we calculated in the last step. The test statistic is : -1/.375 = -2.67.

Looking up this value in a t-table or using the Excel function =TDIST(2.67,63,2) we get a p-value of .01. Because the p-value is less than out alpha we reject the Null Hypothesis that the population mean is 50. We come to the same conclusion as we did with the confidence interval--it is unlikely the population mean is 50.

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