ab-testing-kit 0.1.4

Useful functions for A/B testing and data exploration

A-B_testing_kit

ab-testing-kit is a Python package designed to facilitate A/B testing for data analysis. It provides utilities to perform statistical tests to compare two groups and determine if there are significant differences between them.

Features

• Normality Test: Check if the data in both test and control groups follow a normal distribution using the Shapiro-Wilk test.
• Variance Equality Test: Assess if the variances between two groups are equal using Levene’s test.
• A/B Testing: Perform A/B testing using Student’s T-test, Welch’s T-test, or Mann-Whitney U test based on the data characteristics.

Installation

You can install ab-testing-kit from PyPI using pip:

pip install ab-testing-kit

Usage

Here's a quick guide on how to use ab-testing-kit for performing A/B testing.

Importing the Package

from ab_testing_kit import ab_test

Sample data

test_group = pd.DataFrame({ 'metric': [2.5, 3.6, 3.8, 2.9, 3.4] })

control_group = pd.DataFrame({ 'metric': [3.2, 3.3, 2.8, 3.0, 3.1] })

Perform A/B testing

`result = ab_test(test_group, control_group, column='metric', alpha=0.05, center='mean')

print("Statistic:", result['statistic']) print("P-value:", result['pvalue'])`

Functions

_normality_test(test, control, column, alpha) Tests if the data in both test and control groups are normally distributed.