7. review.py

import numpy as np
import pandas as pd
from scipy.stats import chi2_contingency

# preset values
significance_threshold = 0.05
sample_size = 180
lift = .3
control_rate = .5
name_rate = (1 + lift) * control_rate

# initialize an empty list of results
results = []

# start the loop
for i in range(100):
  # simulate data:
  sample_control = np.random.choice(['yes', 'no'],  size=int(sample_size/2), p=[control_rate, 1-control_rate])
  sample_name = np.random.choice(['yes', 'no'], size=int(sample_size/2), p=[name_rate, 1-name_rate])
  group = ['control']*int(sample_size/2) + ['name']*int(sample_size/2)
  outcome = list(sample_control) + list(sample_name)
  sim_data = {"Email": group, "Opened": outcome}
  sim_data = pd.DataFrame(sim_data)

  # run the test
  ab_contingency = pd.crosstab(np.array(sim_data.Email), np.array(sim_data.Opened))
  chi2, pval, dof, expected = chi2_contingency(ab_contingency)
  result = ('significant' if pval < significance_threshold else 'not significant')

  # append the result to our results list:
  results.append(result)

# calculate proportion of significant results:
print("Proportion of significant results:")
results =  np.array(results)
print(np.sum(results == 'significant')/100)