Final answer:
With a larger sample and a more diverse range of data measured, the distribution becomes more normal due to the Central Limit Theorem, with reduced skewness and potentially a smaller standard deviation.
Step-by-step explanation:
When measuring a wider range of things using a larger sample, the distribution tends to become more normal (option D). This phenomenon is explained by the Central Limit Theorem, which states that as the sample size increases, the sampling distribution of the mean will become more normally distributed, regardless of the shape of the original population distribution. This is because, with a larger sample, there is a more comprehensive representation of all possible outcomes, which tends to reduce skewness and produce a distribution that is more symmetric around the mean.
Skewness is reduced because as you include more varied data by measuring a wider range of things, extreme values or outliers have less effect on the overall shape of the distribution. Overall, a larger sample size can lead to a distribution with a smaller standard deviation, which makes it narrower and taller. However, this does not mean the range of the data is narrower; the variability or dispersion of data around the mean is less.