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This Streamlit application simulates A/B tests, providing a platform to evaluate the performance of different statistical tests based on data distribution.

Home Page: https://ab-simulator.streamlit.app/

Python 100.00%
ab-testing experiment-design hypothesis-testing poisson-bootstrap streamlit t-test

ab-test-simulator's Introduction

Streamlit AB-test Simulator

This Streamlit app allows you to simulate A/B tests for evaluating the performance of different versions of a web page or application. It generates synthetic data for A/B testing based on user-defined parameters and provides various statistical analyses and visualizations to interpret the results.

AB-test simulator UI

Features

  • Data Generation Model: Customize the parameters for generating synthetic data including base click-through rate (CTR), CTR uplift, skewness, and beta distribution parameters.
  • Experiment Design: Specify the significance level, power, and minimum detectable effect to design your A/B tests.
  • Ground Truth Distributions: Visualize the distributions of CTR and views for control and treatment groups under the null and alternative hypotheses.
  • A/B Tests Results: Conduct various statistical tests including t-tests, Mann-Whitney U tests, and binomial tests to compare the performance of control and treatment groups. Visualize the distributions and empirical cumulative distribution functions (CDFs) of p-values.
  • Statistical Power Analysis: Evaluate the statistical power of the conducted tests to detect significant differences between groups.

How to Use

  1. Data Generation Model: Adjust the sliders in the sidebar to customize the parameters for generating synthetic data.
  2. Experiment Design: Set the significance level, power, and minimum detectable effect for designing your A/B tests.
  3. Click "Apply" to generate the synthetic data and estimate the parameters.
  4. Review Ground Truth Distributions: Examine the distributions of CTR and views under the null and alternative hypotheses.
  5. Conduct A/B Tests: Explore the results of various statistical tests and visualizations.
  6. Interpret Results: Analyze the p-value distributions and statistical power to draw conclusions about the effectiveness of the tested variations.

Installation

To run this Streamlit app locally, follow these steps:

  1. Clone this repository:
git clone https://github.com/insdout/AB-test-simulator.git
  1. Navigate to the project directory:
cd AB-test-simulator
  1. Install the required dependencies:
pip install -r requirements.txt
  1. Run the Streamlit app:
streamlit run streamlit_app.py
  1. Access the app in your web browser at http://localhost:8501.

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