Simple Random Sample vs Cluster Sample
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.
Simple random sample
Collecting data and study design
A simple random sample is selected so that every possible sample of the given size has an equal chance of being the one chosen.
In a simple random sample, often shortened to SRS, each individual and each group of individuals is equally likely to be picked. For example, numbering all 500 students at a school and using a random number generator to select 30 of them produces an SRS. This equal-chance selection removes selection bias and is what justifies the standard error formulas used later in inference. It usually requires a list of the whole population to sample from.
Cluster sample
Collecting data and study design
A cluster sample divides the population into groups called clusters, randomly selects whole clusters, and includes every individual in the chosen clusters.
Cluster sampling is used when the population naturally falls into groups that are each meant to resemble the whole population. For example, to survey a school district you might randomly choose 5 schools and then survey every student in those 5 schools. Unlike strata, clusters are ideally diverse within and similar to one another, and you sample entire clusters rather than individuals. It is often cheaper because you only travel to a few locations.