The purpose of this task is to study an example where the assumptions of a hypothesis test are not met. In particular, we wish to see how this affects the probability of a type I error.

First, let us simulate a population of people's ages. According to the CIA World Factbook, the proportion of people in the world in the following age bands are as given below:

14 years old or younger | 25.44% |

15 to 24 years old | 16.16% |

25 to 54 years old | 41.12% |

55 to 64 years old | 8.6% |

65 to 84 years old | 8.68% |

For simplicity, let's assume that we will not simulate anyone older than 84 years old.

Put this information into chart form, as shown below (with a green header), and then use it to generate an "age" population in the following way: select a random age band "group" in accordance with the probabilities just given, and then pick a uniformly random age in this age band. For convenience, you can list the group chosen in the column just left of the column corresponding to the ages chosen. (Remember the VLOOKUP() and INDEX() functions, they may be helpful!)

Now, add another sheet and randomly sample from the "Age" column to make a table that is 50 columns by 10,000 rows. (Use F5 to select this large array. Control D and Control R will fill a formula down and to the right, respectively.) This represents 10,000 samples of size up to 50.

Our intention is - for EACH of these samples - to test whether they suggest there is evidence that the population mean is not what it actually is. To help in the calculations needed for all of these tests, let us create cells (colored blue below) in a third sheet that contain the desired sample size (30), the significance level (0.05) for our tests, and to calculate the population mean.

Find the mean and standard deviation of each of the 10,000 samples using the sample size entered. Then calculate each of the t-test statistics, the P-values, and decide whether to reject the null hypothesis. (Remember to reference the blue cells, as you make your calculations.)

In another cell (colored red below), find the proportion of samples for which the null hypothesis is rejected.

For what proportion of samples did you expect the null hypothesis to be rejected? Carefully explain why.

Compare your results to your expectation. Do they seem close?

In order to evaluate how close the results are to the expectation, construct a confidence interval for the proportion of samples rejected by the hypothesis test. Select a cell to enter the confidence level (95%), then (using as many cells as you need for intermediate calculations) find the lower and upper bounds for the confidence interval. (These calculations are shown in purple above.)

Recalculate your spreadsheet several times using sample size 50 and again using sample size 10.

For sample size 50, what value do you expect to find in the confidence interval? Is it usually in the interval?

For sample size 10, what value do you expect to find in the confidence interval? Is it usually in the interval?

How does all of this relate to the probability of a type I error and whether or not the assumptions for the relevant hypothesis test are met?