Replication vs Random Assignment
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.
Replication
Collecting data and study design
Replication means assigning each treatment to more than one experimental unit, so treatment effects can be told apart from unit-to-unit variation.
Replication is about the number of units per treatment inside one study, not about repeating the whole study later. For example, giving a drug to 30 patients and a placebo to 30 patients replicates each treatment 30 times, while giving each to a single patient does not replicate at all. Without replication, one unusual unit looks exactly like a treatment effect. More units per treatment also makes the comparison between the groups more precise.
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.