Get intuitive of how sample is important to a population data sets

comparemean.py

import random

population = [random.randint(70, 100) for n in range(100)]

def comparePopulationMeanAndSampleMean(population, numberOfSample):
    populationMean = sum(population)/population.__len__()
    random.shuffle(population)
    sample = population[:numberOfSample]
    sampleMean = sum(sample)/sample.__len__()
    print "population mean: %s \nsample mean: %s" % (populationMean, sampleMean)
    
def runTest():
    # run 20 tests, change test respectively
    for i in range(20):
        #get number of samples that going to used, max is half of sample, minimal is 20
        numberOfSample = random.randint(20, round(population.__len__()/2))
        print "Test number %d, number of samples is %s, data taken randomly from population" % (i+1, numberOfSample)
        comparePopulationMeanAndSampleMean(population, numberOfSample)
        
if __name__ == '__main__':
    print "data population", population
    runTest()