Exercises: Decide if a Model Is Consistent with Data Using Simulation
Work through each section in order. A "model" is an assumed chance
mechanism (for example, "this coin lands heads with probability 0.5").
To decide if data is consistent with a model, assume the model is true,
simulate the data-generating process many times, build the simulated
distribution, and see how often the model produces a result as extreme
as the one observed. Remember: a surprising result is EVIDENCE to
question a model, never proof the model is false; and an ordinary result
never proves a model true.
Warm-Up: Models, Chance, and Distributions
These problems review ideas you already know about chance and distributions.
A free-throw shooter claims she makes of her shots. In one session she takes 20 shots. You want to simulate this session to see how many makes are typical. What should one trial of your simulation produce?