Random Assignment vs Stratified 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.
Stratified random sample
Collecting data and study design
A stratified random sample splits the population into similar groups called strata, then takes a separate simple random sample from each stratum.
Stratifying groups individuals who resemble each other on something relevant, then samples within every group, which can give more precise estimates than a plain simple random sample. For example, you might split students into grade levels and draw a random sample from each grade so every grade is represented. The strata should be internally similar but different from one another. You then combine the results across strata to estimate the population value.