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//simulating a binomial distribution (flipping an evenly weighted coin 20 times, for 1000 trials). 
this can be assigned to a list of values where you can then query the probability of any given scenario
x = np.random.binomial(20,0.5, 1000)
print(x>15).mean()

//simulating a random distribution
distribution = np.random.normal(0.75,size=1000)
np.std(distribution)

//t-test scipy 
from scipy import stats
stats.ttest_ind?

//hold out testing or bonferroni correction. bonferroni correction is considered somewhat conservative.