Creating a sampling distribution

Sampling in Python

James Chapman

Curriculum Manager, DataCamp

Same code, different answer

coffee_ratings.sample(n=30)['total_cup_points'].mean()

82.53066666666668

coffee_ratings.sample(n=30)['total_cup_points'].mean()

81.97566666666667

coffee_ratings.sample(n=30)['total_cup_points'].mean()

82.68

coffee_ratings.sample(n=30)['total_cup_points'].mean()

81.675

Same code, 1000 times

mean_cup_points_1000 = []

for i in range(1000):
    mean_cup_points_1000.append(
        coffee_ratings.sample(n=30)['total_cup_points'].mean()
    )

print(mean_cup_points_1000)

[82.11933333333333, 82.55300000000001, 82.07266666666668, 81.76966666666667, 
...
 82.74166666666666, 82.45033333333335, 81.77199999999999, 82.8163333333333]

Distribution of sample means for size 30

import matplotlib.pyplot as plt
plt.hist(mean_cup_points_1000, bins=30)
plt.show()

A sampling distribution is a distribution of replicates of point estimates.

A histogram of sample means.

Different sample sizes

Sample size: 6

A histogram of sample means with a sample size of six.

Sample size: 150

A histogram of sample means with a sample size of 150.

Let's practice!

Sampling in Python