###################################################################
# Flipping a biased coin

from collections import Counter
from random import choices

print(Counter(choices(range(2), [0.9, 0.1], k=1000)))

###################################################################
# Bootstrapping

'From a small statistical sample infer a 90% confidence interval for the mean'
# http://statistics.about.com/od/Applications/a/Example-Of-Bootstrapping.htm

from statistics import mean
from random import choices

data = 1, 2, 4, 4, 10
means = sorted(mean(choices(data, k=5)) for i in range(20))
print('The sample mean of {:.1f} has a 90% confidence interval from {:.1f} to {:.1f}'.format(
  mean(data), means[1], means[-2]))