Final answer:
A simple random sample without replacement of size 44 can be created using a random number generator. Different samples analyzed with the compute_statistics function will result in varying histograms and statistics, potentially leading to changes in the average age due to sampling variability.
Step-by-step explanation:
A simple random sample without replacement of size 44 can be obtained by using a random number generator to select 44 distinct items from the full_data. This could involve programming tools such as those available in statistical software like R or Python's pandas library, or even utilizing spreadsheet functions. After creating the sample, you would use the given compute_statistics function to analyze this sample. The resulting histograms and statistics will likely vary with each different sample because each sample may not perfectly represent the population's parameters.
The average age across different samples can change due to the random variation inherent in sampling. In a normally distributed population with a known mean and standard deviation, the variation can be predicted and is explained by the distribution's properties and the central limit theorem. However, without knowing the specific values from the full_data, we cannot quantify the exact change in average age, but we can expect it to fall within a certain interval based on the population's standard deviation.
To assess whether the average age changes significantly across samples, you might perform a hypothesis test. The null hypothesis (H0) in this case might be that there is no change in the average age across samples, while the alternative hypothesis (H1) would be that there is a significant change.