Simulation vs Experiment
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.
Simulation
Probability
A simulation imitates a chance process using repeated random outcomes, then estimates a probability from the fraction of trials that give the event.
A simulation lets you approximate a probability that would be hard to compute directly by running the random process many times. For example, to estimate the chance of at least one head in three coin flips, you can generate many sets of three random 0s and 1s and record how often a set contains a 1. The estimate is the number of successful trials divided by the total number of trials, (p-hat, the estimated probability). Running more trials generally gives an estimate closer to the true probability.
Experiment
Collecting data and study design
An experiment imposes treatments on subjects and compares their responses, supporting a cause-and-effect conclusion when treatments are assigned at random.
In an experiment the researcher actively assigns treatments rather than just observing what happens. For example, randomly assigning patients to take a new drug or a placebo and then comparing recovery rates is an experiment. Random assignment balances out other variables across the groups, which is what lets a difference in response be attributed to the treatment. Its key ideas are comparison, random assignment, and replication across many subjects.