Effect Size

Credits: Forked from CompStats by Allen Downey. License: Creative Commons Attribution 4.0 International.

from __future__ import print_function, division

import numpy
import scipy.stats

import matplotlib.pyplot as pyplot

from IPython.html.widgets import interact, fixed
from IPython.html import widgets

# seed the random number generator so we all get the same results
numpy.random.seed(17)

# some nice colors from http://colorbrewer2.org/
COLOR1 = '#7fc97f'
COLOR2 = '#beaed4'
COLOR3 = '#fdc086'
COLOR4 = '#ffff99'
COLOR5 = '#386cb0'

%matplotlib inline

To explore statistics that quantify effect size, we'll look at the difference in height between men and women. I used data from the Behavioral Risk Factor Surveillance System (BRFSS) to estimate the mean and standard deviation of height in cm for adult women and men in the U.S.

I'll use scipy.stats.norm to represent the distributions. The result is an rv object (which stands for random variable).

mu1, sig1 = 178, 7.7
male_height = scipy.stats.norm(mu1, sig1)

mu2, sig2 = 163, 7.3
female_height = scipy.stats.norm(mu2, sig2)