old_contents: from math import factorial as fact
  from random import random, randrange, shuffle
  from statistics import mean
  
  def combinations(n, r):
      return fact(n) / fact(r) / fact(n-r)
  
  def cum_binom(n, r, ph):
      total = 0.0
      for h in range(r, n+1):
          total += ph ** h * (1.0 - ph) ** (n - h) * combinations(n, h)
      return total
  
  # Estimate the probability of getting 42500 or more heads
  # from 70000 spins of biased coin (60% heads and 40% tails)    
  
  # Estimate the probability of getting 5 or more heads
  # from 7 spins of biased coin (60% heads and 40% tails)
  print(cum_binom(7, 5, 0.60))
  
  def trial():
      nh = 0
      for i in range(7):
          nh += random() < 0.60
      return nh >= 5
  
  cases = 0
  for i in range(10000):
      cases += trial()
  print(cases / 10000.0)
  
  trial = lambda : sum(random() < 0.60 for i in range(7)) >= 5
  print(sum(trial() for i in range(10000)) / 10000.0)
  
  # Probability of the median of 5 samples being in the
  # middle two quartiles of the population
  trial = lambda :  2500 <= sorted(randrange(10000) for i in range(5))[2] < 7500
  print(sum(trial() for i in range(10000)) / 10000.0)    # compare with the beta distribution
  
  # An exact permutation text
  # Hypothesis testing
  drug = [54, 73, 53, 70, 73, 68, 52, 65, 65]
  placebo = [54, 51, 58, 44, 55, 52, 42, 47, 58, 46]
  obs_diff = mean(drug) - mean(placebo)
  
  # Null hypothesis is that there is no real difference between the drug
  # and the placebo.  Which means that any observed difference was just
  # due to chance and noise.
  
  comb = drug + placebo
  
  cases = 0
  for i in range(10000):
      shuffle(comb)
      new_diff = mean(comb[:len(drug)]) - mean(comb[len(drug):])
      cases += new_diff >= obs_diff
  print(cases / 10000, '<-- The p-value')
  print('Accordlingly, we reject the null hypothesis')
  print('and conclude the observed difference was not due to chance')
  
new_contents: from math import factorial as fact
  from random import random, randrange, shuffle
  from statistics import mean
  
  def combinations(n, r):
      return fact(n) // (fact(r) * fact(n-r))
  
  def cum_binom(n, r, ph):
      total = 0.0
      for h in range(r, n+1):
          total += ph ** h * (1.0 - ph) ** (n - h) * combinations(n, h)
      return total
  
  # Estimate the probability of getting 42500 or more heads
  # from 70000 spins of biased coin (60% heads and 40% tails)    
  
  # Estimate the probability of getting 5 or more heads
  # from 7 spins of biased coin (60% heads and 40% tails)
  print(cum_binom(7, 5, 0.60))
  
  def trial():
      nh = 0
      for i in range(7):
          nh += random() < 0.60
      return nh >= 5
  
  cases = 0
  for i in range(10000):
      cases += trial()
  print(cases / 10000.0)
  
  trial = lambda : sum(random() < 0.60 for i in range(7)) >= 5
  print(sum(trial() for i in range(10000)) / 10000.0)
  
  # Probability of the median of 5 samples being in the
  # middle two quartiles of the population
  trial = lambda :  2500 <= sorted(randrange(10000) for i in range(5))[2] < 7500
  print(sum(trial() for i in range(10000)) / 10000.0)    # compare with the beta distribution
  
  # An exact permutation text
  # Hypothesis testing
  drug = [54, 73, 53, 70, 73, 68, 52, 65, 65]
  placebo = [54, 51, 58, 44, 55, 52, 42, 47, 58, 46]
  obs_diff = mean(drug) - mean(placebo)
  
  # Null hypothesis is that there is no real difference between the drug
  # and the placebo.  Which means that any observed difference was just
  # due to chance and noise.
  
  comb = drug + placebo
  
  cases = 0
  for i in range(10000):
      shuffle(comb)
      new_diff = mean(comb[:len(drug)]) - mean(comb[len(drug):])
      cases += new_diff >= obs_diff
  print(cases / 10000, '<-- The p-value')
  print('Accordlingly, we reject the null hypothesis')
  print('and conclude the observed difference was not due to chance')
  
subject: Use integer math for combinations()