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  3. Statistical Thinking in Python (Part 1)

Exercise

The np.random module and Bernoulli trials

You can think of a Bernoulli trial as a flip of a possibly biased coin. Specifically, each coin flip has a probability \(p\) of landing heads (success) and probability \(1-p\) of landing tails (failure). In this exercise, you will write a function to perform n Bernoulli trials, perform_bernoulli_trials(n, p), which returns the number of successes out of n Bernoulli trials, each of which has probability p of success. To perform each Bernoulli trial, use the rng.random() function, which returns a random number between zero and one.

Instructions

100 XP
  • Define a function with signature perform_bernoulli_trials(n, p).
    • Initialize to zero a variable n_success the counter of Trues, which are Bernoulli trial successes.
    • Write a for loop where you perform a Bernoulli trial in each iteration and increment the number of success if the result is True. Perform n iterations by looping over range(n).
      • To perform a Bernoulli trial, choose a random number between zero and one using rng.random(). If the number you chose is less than p, increment n_success (use the += 1 operator to achieve this). An RNG has already been instantiated as the variable rng and seeded.
    • The function returns the number of successes n_success.