Sample Size Calculation

How do we decide on how long a test should run, or in our terms, how many observations do we need per group? This question is relevant because it's normally advised that you decide on a sample size before you start an experiment. While many A/B testing guides attempt to provide general advice, the reality is that it varies case by case. A common approach for overcoming this problem referred to as the power analysis.

Power Analysis

We perform power analysis to generate needed sample size, and the it includes the following metrics:

  1. Effect size (calculated via lift): the minimum size of the effect that we want to detect in a test; for example, a 5% increase in conversion rates.

    1. For testing the differences in means, after selecting the suitable minimum detectable effect(MDE) of interest, we convert it into a standardized effect size known as Cohen's d defined as the difference between the two means divided by the standard deviation:

      Cohen 's d = (µB -µA) / stdev_pooled

    2. For differences in proportions, a common effect size to use is Cohen's h calculated using the formula:

      Cohen' s h = 2 arcsin (sqrt(p1)) - 2 arcsin (sqrt(p2))

    A general rule of thumb:

    • 0.2 corresponds to a small effect,

    • 0.5 is a medium effect,

    • 0.8 is large.

  2. Significance Level (predetermined): Alpha value; 5% is typical.

  3. Power (predetermined): Probability of detecting an effect

Keep in mind that if we change any of the above metrics, the needed Sample size also changes.

More power, a smaller significance level, or detecting a smaller effect all lead to a larger sample size. 

"""
The power functions require standardized minimum effect difference. 
To get this, we can use the proportion_effectsize function by inputting our baseline 
and desired minimum conversion rates.
"""
from statsmodels.stats.proportion import proportion_effectsize
from statsmodels.stats.power import zt_ind_solve_power

# calculate standardized minimum effect difference. 
std_effect = proportion_effectsize(0.15, 0.1)

# calculate sample size with alpha=.05, and varying powers
sz1 = zt_ind_solve_power(effect_size=std_effect, nobs1=None, alpha=.05, power=.80)
sz2 = zt_ind_solve_power(effect_size=std_effect, nobs1=None, alpha=.05, power=.90)

print(f"{sz1:.2f}")
print(f"{sz2:.2f}")

"""
680.35
910.80

Note that increasing Power required more samples.
"""

Effect Size, Sample Size and the Power

Below is the Power of Test graph with varying sample and effect sizes:

Code to produce the image above:

import numpy as np
import matplotlib.pyplot as plt
from statsmodels.stats.power import TTestIndPower

# Sample Size and Effect Size
sample_sizes = np.array(range(5, 100))
effect_sizes = np.array([0.2, 0.5, 0.8])

# Create results object for t-test analysis
res = TTestIndPower()

# Plot the power analysis
res.plot_power(dep_var='nobs', nobs=sample_sizes, effect_size=effect_sizes)
plt.show()

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