Exercises for Section 3.5 Two-sample tests

This notebook contains the solutions to the exercises from Section 3.5 Two-sample tests in the No Bullshit Guide to Statistics.

Notebooks setup

import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

Estimator functions defined in Section 3.1

def mean(sample):
    return sum(sample) / len(sample)

def var(sample):
    xbar = mean(sample)
    sumsqdevs = sum([(xi-xbar)**2 for xi in sample])
    return sumsqdevs / (len(sample)-1)

def std(sample):
    s2 = var(sample)
    return np.sqrt(s2)

def dmeans(xsample, ysample):
    dhat = mean(xsample) - mean(ysample)
    return dhat

Exercises

E1. Permutation test for the sleep score sin the doctors

doctors = pd.read_csv("../datasets/doctors.csv")
scoresU = doctors[doctors["loc"]=="urb"]["score"]
scoresR = doctors[doctors["loc"]=="rur"]["score"]

# observed difference between means
dhat = dmeans(scoresR, scoresU)
dhat

6.992885375494076

from ministats import permutation_test_dmeans
pvalue = permutation_test_dmeans(scoresR, scoresU)
pvalue

0.0525

# ALT. use the ttest_ind with permutations argument
from scipy.stats import ttest_ind
ttest_ind(scoresR, scoresU, permutations=10000).pvalue

0.05299470052994701

E2. Sleep scores using t-test

E3. Example 6T with pooled variance

Redo Example 6T but this time run the two-sample \(t\)-test with pooled variance.

students = pd.read_csv("../datasets/students.csv")
scoresD = students[students["curriculum"]=="debate"]["score"]
scoresL = students[students["curriculum"]=="lecture"]["score"]
res = ttest_ind(scoresD, scoresL, equal_var=True)
res.pvalue

0.10917234443214315

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