Screen Link:

My Code:

‘’’

import numpy as np

import matplotlib.pyplot as plt

chi_squared_values =

for n in range(1000):

random_numbers = np.random.choice([0, 1], size=32561, replace=True)

binary_counts = np.bincount(random_numbers)

male_count = binary_counts[0]

female_count = binary_counts[1]

male_diff = ((male_count - 16280.5) ** 2) / 16280.5

female_diff = ((female_count - 16280.5) ** 2) / 16280.5

chi_squared = male_diff + female_diff

chi_squared_values.append(chi_squared)

plt.hist(chi_squared_values)

‘’’

What I expected to happen:

The corrrect chi_squared_values

What actually happened:

Different values

I (sloppily) recreated the above code using np.random.rand() and I got the correct values, but I don’t understand what happens under the hood to give a different output. Correct code:

‘’’

import numpy as np

import matplotlib.pyplot as plt

chi_squared_values =

for n in range(1000):

random_numbers = np.random.rand(32561)

binary_numbers =

for number in random_numbers:

if number >= 0.5:

number = 1

binary_numbers.append(number)

else:

number = 0

binary_numbers.append(number)

male_count = binary_numbers.count(0)

female_count = binary_numbers.count(1)

male_diff = ((male_count - 16280.5) ** 2) / 16280.5

female_diff = ((female_count - 16280.5) ** 2) / 16280.5

chi_squared = male_diff + female_diff

chi_squared_values.append(chi_squared)

plt.hist(chi_squared_values)

‘’’