Aside: Fast Permutation Tests

import numpy as np
import pandas as pd
import os

import plotly.express as px
pd.options.plotting.backend = 'plotly'

Speeding things up 🏃

Speeding up permutation tests

• A permutation test, like all simulation-based hypothesis tests, generates an approximation of the distribution of the test statistic.
  • If we found all permutations, the distribution would be exact!
  • If there are $a$ elements in one group and $b$ in the other, the total number of permutations is ${a + b \choose a}$.
  • If $a = 100$ and $b = 150$, there are more than ${250 \choose 100} \approx 6 \cdot 10^{71}$ permutations!

• The more repetitions we use, the better our approximation will be.
• Unfortunately, our code is pretty slow, so we can't use many repetitions.

Example: Birth weight and smoking 🚬

baby_fp = os.path.join('data', 'baby.csv')
baby = pd.read_csv(baby_fp)
baby = baby[['Maternal Smoker', 'Birth Weight']]

baby.head()

	Maternal Smoker	Birth Weight
0	False	120
1	False	113
2	True	128
3	True	108
4	False	136

Recall our permutation test from last class:

• Null Hypothesis: In the population, birth weights of smokers' babies and non-smokers' babies have the same distribution, and the observed differences in our samples are due to random chance.
• Alternative Hypothesis: In the population, smokers' babies have lower birth weights than non-smokers' babies, on average. The observed difference in our samples cannot be explained by random chance alone.

Timing the birth weights example ⏰

We'll use 3000 repetitions instead of 500.

%%time

n_repetitions = 3000

differences = []
for _ in range(n_repetitions):
    
    # Step 1: Shuffle the weights and store them in a DataFrame.
    with_shuffled = baby.assign(Shuffled_Weights=np.random.permutation(baby['Birth Weight']))

    # Step 2: Compute the test statistic.
    # Remember, alphabetically, False comes before True,
    # so this computes True - False.
    group_means = (
        with_shuffled
        .groupby('Maternal Smoker')
        .mean()
        .loc[:, 'Shuffled_Weights']
    )
    difference = group_means.diff().iloc[-1]
    
    # Step 3: Store the result.
    differences.append(difference)

CPU times: user 2.04 s, sys: 3.15 ms, total: 2.05 s
Wall time: 2.05 s

fig = px.histogram(pd.DataFrame(differences), x=0, nbins=50, histnorm='probability', 
                   title='Empirical Distribution of the Test Statistic, Original Approach')
fig.update_layout(xaxis_range=[-5, 5])