random sampling- calculate avg mean and avg std in Python

'''
Random Sampling from a Population
From lecture, we know that Simple Random Sampling (SRS) has the following properties:

    Start with known list of N* population units, and randomly select *n units from the list
    Every unit has equal probability of selection = n/N
    All possible samples of size n are equaly likely
    Estimates of means, proportions, and totals based on SRS are UNBIASED (meaning they are equal to the population values on average)
'''
mu = 0
sigma = 1
Population = [random.normalvariate(mu, sigma) for _ in range(10000)]

SampleA = random.sample(Population, 500)
SampleB = random.sample(Population, 500)

np.mean(SampleA)
np.std(SampleA)

np.mean(SampleB)
np.std(SampleB)

# average mean from 100 random samples with size = 1000
means = [np.mean(random.sample(Population, 1000)) for _ in range(100)]
np.mean(means)  # very close to 0

standarddevs = [np.std(random.sample(Population, 1000)) for _ in range(100)]
np.mean(standarddevs) # very close to 1