Sampling Distribution vs Distribution
Both terms below come up in the same part of the course, and students mix them up. Here is each one defined on its own, side by side, so you can see where they part company.
Sampling distribution
Sampling distributions
A sampling distribution is the distribution of a statistic across all possible samples of the same size drawn from a population.
A sampling distribution describes how a statistic, such as the sample mean, changes from one random sample to the next. For example, taking many samples of 40 students and recording each sample mean builds up the sampling distribution of (x-bar, the sample mean). For the sample mean its center is the population mean (mu) and its spread, the standard error, is (sigma over the square root of n). This spread shrinks as the sample size grows, so larger samples give more consistent estimates.
Distribution
Describing data
A distribution describes the possible values a variable takes and how often each value or range of values occurs.
A distribution tells you the pattern of a variable: its center, its spread, and its overall shape. For example, the distribution of quiz scores might cluster near 8 out of 10 with a few low outliers. You often summarize a distribution with a center like the mean or median and a spread like the standard deviation or range. Graphs such as dotplots, histograms, and boxplots let you see a distribution's shape at a glance.