Random Assignment vs Simple Random 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.
Random assignment
Collecting data and study design
Random assignment uses chance to sort experimental units into treatment groups, so the groups start out similar and confounding is balanced out.
Random assignment is how an experiment builds comparable groups. Because a coin flip or random number decides who gets which treatment, no systematic difference in age, health, or motivation piles up in one group, so a later difference in outcomes can be credited to the treatment. For example, flipping a coin for each of 40 patients to assign a drug or a placebo balances the other traits across the two groups on average. Random assignment builds the groups, while random selection picks the sample; assignment supports cause-and-effect claims, and selection supports generalizing to a population.
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.