Sample means follow a normal distribution
In the previous exercise, we generated a population that followed a binomial distribution, chose 20 random samples from the population, and calculated the sample mean. Now we're going to test some other probability distributions to see the shape of the sample means.
From the scipy.stats library, we've loaded the poisson and geom objects and the describe() function. We've also imported matplotlib.pyplot as plt and numpy as np.
As you'll see, the shape of the distribution of the means is the same even though the samples are generated from different distributions.
This exercise is part of the course
Foundations of Probability in Python
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Generate the population
population = geom.rvs(p=0.5, size=1000)
# Create list for sample means
sample_means = []
for _ in range(3000):
# Take 20 values from the population
sample = np.random.choice(population, ____)
# Calculate the sample mean
sample_means.append(describe(____).____)
# Plot the histogram
plt.____(sample_means)
plt.show()