Question

How is a sampling distribution of means different from a distribution of raw scores?

Answer 1

Sampling distribution is when you continuously take samples of a given size. The raw scores are the actual data collected. For example with a population of150,000, the sampling distribution of means would be if you repeatedly took samples of 10 people and complied the results. The raw scores would be theactual 150 000 individual data points of the population.

Answer 2

The sampling distribution is a distribution of a sample statistic. It is a model of a distribution of scores, like the population distribution, except thatthe scores are not raw scores, but statistics. It is a thought experiment; "what would the world be like if a person repeatedly took samples of size N fromthe population distribution and computed a particular statistic each time?" The resulting distribution of statistics is called the sampling distribution ofthat statistic.

For example, suppose that a sample of size sixteen (N=16) is taken from some population. The mean of the sixteen numbers is computed. Next a new sample ofsixteen is taken, and the mean is again computed. If this process were repeated an infinite number of times, the distribution of the now infinite number ofsample means would be called the sampling distribution of the mean.

Every statistic has a sampling distribution. For example, suppose that instead of the mean, medians were computed for each sample. The infinite number ofmedians would be called the sampling distribution of the median.