Why is the Central Limit Theorem so important to statistical analysis? How does the Central Limit Theorem help us when we are testing hypotheses about sample means? Reflect on what happens to the shape of a graph when the number of samples increases and number of data in each sample increases - i.e., how does an increase in sample affect the distribution mean and standard deviation? Try to relate ideas in this discussion to the analysis of results from simulation models.