Sample Space 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.
Sample space
Probability
The sample space is the set of all possible outcomes of a random process or experiment.
The sample space lists every outcome that could occur, and the probabilities across it sum to 1. For example, the sample space for flipping two coins is , four equally likely outcomes. An event is any subset of the sample space, such as getting exactly one head. Writing out the sample space is often the first step in computing a probability.
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.