MLE Bernoulli Standard Error

Problem

The standard error of the Bernoulli success-probability MLE is sqrt(p_hat (1 - p_hat) / n), the square root of the inverse Fisher information.

Given p_hat and n, return the standard error.

Hint

The variance of the score determines the asymptotic standard error.

case 1

in: [0.5,100]

expected: 0.05

case 2

in: [0.2,50]

expected: 0.05656854249492381

case 3

in: [0.9,10]

expected: 0.09486832980505137

Starter codePython

def mle_bernoulli_standard_error(p_hat, n):
    # Your code here
    pass

3 test cases · run your code against them in the editor