Advanced A/B Testing Statistical Analytics
Project description
ABalytics: Advanced A/B Testing Statistical Analytics
ABalytics is a Python package designed for statistical analysis, particularly for assessing the significance of A/B testing results. Its goal is to provide high-quality analysis by selecting the appropriate statistical tests based on the type of variable being analyzed. It offers a suite of tools to perform various significance tests and posthoc analyses on experimental data.
Features
- Boolean and Numeric Analysis: Supports analysis of both boolean and numeric data types, ensuring the use of correct statistical methods for each.
- Significance Tests: Includes a variety of significance tests such as Chi-Square, Welch's ANOVA, and Kruskal-Wallis, to accurately determine the significance of results.
- Posthoc Analysis: Offers posthoc analysis methods like Tukey's HSD, Dunn's test, and Games-Howell, for detailed examination following significance tests.
- Normality and Homogeneity Checks: Performs checks for Gaussian distribution and homogeneity of variances using Levene's test, which are critical for selecting the right tests.
- Pretty Text Output: Generates a formatted text output with the results of the statistical tests, facilitating interpretation and reporting.
Installation
To install ABalytics, use pip:
pip install abalytics
Usage
To use ABalytics, import it:
import abalytics
An example of how to use ABalytics can be found in examples/example.py
.
Contributing
Contributions to ABalytics are welcome.
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