CLT

The central limit theorem states that a sampling distribution of a sample statistic approaches the normal distribution as you take more samples, no matter the original distribution being sampled from.

This means, if we draw repeated samples enough time:
mean
median
proportion

are all given in a normal distribtution, with average representing the whole population figures. This is regardless of the type of distribution we are drawing our data from: norma, flat, biomial, poisson etc. as long as the draws are independednt


```
# Set seed to 104
np.random.seed(104)

sample_means = []
# Loop 100 times
for i in range(100):
  # Take sample of 20 num_users
  samp_20 = amir_deals['num_users'].sample(20, replace=True)
  # Calculate mean of samp_20
  samp_20_mean = np.mean(samp_20)
  # Append samp_20_mean to sample_means
  sample_means.append(samp_20_mean)
  
# Convert to Series and plot histogram
sample_means_series = pd.Series(sample_means)
sample_means_series.hist()
# Show plot
plt.show()


```