{"id":37071603,"url":"https://github.com/domingosdeeulariadumba/ablisk","last_synced_at":"2026-01-14T08:24:09.162Z","repository":{"id":273597989,"uuid":"919860448","full_name":"domingosdeeulariadumba/ablisk","owner":"domingosdeeulariadumba","description":"A Python module for design, analysis and decision-making of A/B tests.","archived":false,"fork":false,"pushed_at":"2025-12-12T17:49:34.000Z","size":6016,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-12-25T09:47:39.795Z","etag":null,"topics":["ab-testing","data-visualization","statistics"],"latest_commit_sha":null,"homepage":"https://xplendid.streamlit.app","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/domingosdeeulariadumba.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null,"notice":null,"maintainers":null,"copyright":null,"agents":null,"dco":null,"cla":null}},"created_at":"2025-01-21T06:29:14.000Z","updated_at":"2025-12-12T17:49:38.000Z","dependencies_parsed_at":"2025-08-26T21:08:39.705Z","dependency_job_id":null,"html_url":"https://github.com/domingosdeeulariadumba/ablisk","commit_stats":null,"previous_names":["domingosdeeulariadumba/xplendid","domingosdeeulariadumba/ablisk"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/domingosdeeulariadumba/ablisk","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/domingosdeeulariadumba%2Fablisk","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/domingosdeeulariadumba%2Fablisk/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/domingosdeeulariadumba%2Fablisk/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/domingosdeeulariadumba%2Fablisk/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/domingosdeeulariadumba","download_url":"https://codeload.github.com/domingosdeeulariadumba/ablisk/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/domingosdeeulariadumba%2Fablisk/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":28413876,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-01-14T08:16:59.381Z","status":"ssl_error","status_checked_at":"2026-01-14T08:13:45.490Z","response_time":107,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.5:443 state=error: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["ab-testing","data-visualization","statistics"],"created_at":"2026-01-14T08:24:08.459Z","updated_at":"2026-01-14T08:24:09.152Z","avatar_url":"https://github.com/domingosdeeulariadumba.png","language":"Jupyter Notebook","funding_links":["https://ko-fi.com/domingosdeeulariadumba"],"categories":[],"sub_categories":[],"readme":"# ablisk\n\nThis module aims to make it easier for data scientists, analysts, and engineers to conduct statistically sound experiments. At its core is the `ABLisk` class. This class provides tools for designing, analyzing, and simulating A/B tests.\n\n---\n\n## Features\n\n- **Sample Size Calculation**: Calculate the minimum required sample size.\n- **Result Analysis**: Retrieve and visualize experiment results with support for confidence intervals and kernel density estimates (KDEs).\n- **Recommendation**: Generate results summary and recommendations based os results.\n\n---\n\n## Class Overview\n\n### `ABLisk`\nThe main class provides the following methods and attributes:\n\n#### **Initialization**\n```python\nABLisk(bcr, mde, alpha = 0.05, power = 0.8, is_absolute_variation: bool = True, is_two_tailed: bool = True)\n```\n\n- **bcr**: Baseline Conversion Rate (0 \u003c= mde \u003c 1).\n- **mde**: Minimum Detectable Effect (absolute or relative) (0 \u003c mde \u003c 1).\n- **alpha**: Significance level (default: 0.05).\n- **power**: Statistical power (default: 0.8).\n- **is_absolute_variation**: Whether `mde` is absolute (default: True).\n- ** is_two_tailed**: Use two-tailed tests (default: True).\n\n#### **Methods**\n\n1. **`get_sample_size()`**\n   - Calculates the minimum required sample size.\n\n3. **`get_experiment_results(n_ctrl, p_ctrl, n_trmt, p_trmt, plot_type = 'KDE')`**\n   - Analyzes and visualizes results.\n   - Parameters:\n     - `n_ctrl`, `n_trmt`: Sample sizes of the control and treatment groups.\n     - `p_ctrl`, `p_trmt`: Conversion rates for control and treatment groups.\n     - `plot_type`: for visualization(`'KDE'` or `'Confidence Intervals'`) or results summary and recommendations (None).\n\n---\n\n## Usage\n\n### Installation\n\n**`pip install ablisk`**\n\n\n### Importing the module\n\n```python\nfrom ablisk import ABLisk\n```\n\n### Example: Calculate Sample Size\n```python\nab_test = ABLisk(bcr = 0.1, mde = 0.02, alpha = 0.05, power = 0.8)\nsample_size = ab_test.get_sample_size()\nprint(f\"Required Sample Size: {sample_size}\")\n```\n\n### Example: Visualize Experiment Results\n```python\nab_test.get_experiment_results(n_ctrl = 500, p_ctrl = 0.1, n_trmt = 500, p_trmt = 0.12, plot_type = 'Confidence Intervals')\n```\n\n💡 A more detailed example regarding the implementation of this tool is available on its respective \u003cem\u003e \u003ca href = 'https://github.com/domingosdeeulariadumba/ablisk/blob/master/ablisk_examples_notebook.ipynb' target = '_blank'\u003e notebook.\u003c/em\u003e You can view the notebook using nbviewer due to GitHub rendering limitations [here](https://github.com/domingosdeeulariadumba/ablisk/blob/main/ablisk_examples_notebook.ipynb).\n\n---\n\n## License\n\nThis project is licensed under the MIT License. See the `LICENSE` file for details.\n\n---\n\n## Contribution\n\nContributions are welcome! Please fork the repository, create a feature branch, and submit a pull request.\n\n\n\n---\n\n## References\n- FÁVERO, L. P.; BELFIORE, P. \u003cem\u003e \u003ca href = 'https://www.amazon.com.br/Manual-An%C3%A1lise-Dados-Luiz-F%C3%A1vero/dp/8535270876' target = '_blank'\u003e Manual de Análise de Dados: estatística e modelagem\nmultivariada com Excel®, SPSS® e Stata®.\u003c/em\u003e Rio de Janeiro: Elsevier, 2017.\n- GRAVETTER, F. J.; WALLNAU, L. B. \u003cem\u003e \u003ca href = 'https://www.amazon.com/Statistics-Behavioral-Sciences-Standalone-Book/dp/1305504917' target = '_blank'\u003e Statistics for the Behavioral Sciences.\u003c/em\u003e 10th ed. Boston:\nCengage Learning, 2015.\n- SAINANI K. Stanford University. \u003cem\u003e \u003ca href = 'https://www.google.com/url?sa=t\u0026source=web\u0026rct=j\u0026opi=89978449\u0026url=https://web.stanford.edu/~kcobb/hrp259/lecture11.ppt\u0026ved=2ahUKEwin0_6qmsuKAxVHUEEAHSzNEt0QFnoECBUQAQ\u0026usg=AOvVaw16arOYUy8mK6FcYHGblX0m' target = '_blank'\u003e Introduction to Sample Size and Power Calculations\u003c/em\u003e. Last accessed on Dec 28 2024.\n- UDACITY. \u003cem\u003e \u003ca href = 'https://www.udacity.com/course/ab-testing--ud257' target = '_blank'\u003e A/B Testing\u003c/em\u003e. Last accessed on Dec 28 2024.\n  \n---\n## Acknowledgments\n\nThis project would not be possible without the massive contribution of Evan Miller regarding A/B testing methodologies and tools. Refer to his \u003cem\u003e \u003ca href = 'https://www.evanmiller.org/ab-testing/sample-size.html' target = '_blank' a\u003e A/B Testing Sample Size Calculator\u003c/em\u003e for further details.\n\n---\n## Explore the Web App 🌐🚀\n\n**ablisk** is the statistical engine of **[xplendid](https://xplendid.streamlit.app/)** – the AI-assisted web application version for this module.\n\n\n## Networking\n\nConnect with me:\n\n\u003cimg src = 'https://i.postimg.cc/wj3w1mjG/kofi-icon.png' width = '25' height = '25'/\u003e  **[/domingosdeeulariadumba](https://ko-fi.com/domingosdeeulariadumba)**\n\n\u003cimg src = 'https://i.postimg.cc/t4vNmLB0/linktree-icon.png' width = '25' height = '25'/\u003e  **[/domingosdeeulariadumba](https://linktr.ee/domingosdeeulariadumba)**\n\n\u003cimg src = 'https://i.postimg.cc/W1178266/linkedin-icon.png' width = '25' height = '25'/\u003e  **[/domingosdeeulariadumba](https://linkedin.com/in/domingosdeeulariadumba/)**","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdomingosdeeulariadumba%2Fablisk","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdomingosdeeulariadumba%2Fablisk","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdomingosdeeulariadumba%2Fablisk/lists"}