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Statistical Decision Tree

The Statistical Decision Tree is a free interactive web application for students, teachers, and researchers who need orientation when choosing a statistical test or model. It is designed for HCI research and adjacent empirical work, especially during study planning, method teaching, and first-pass analysis decisions. The app guides users through key design questions such as outcome type, number and type of predictors, independent versus paired data, repeated measures, and parametric versus nonparametric routes. It then shows suitable methods together with assumption checks, effect-size notes, follow-up recommendations, and example code in R and Python. The tool is meant as orientation support for students and novice researchers. It does not replace statistical training, supervision or study-specific method consultation.

Swipe horizontally to inspect the full decision tree on smaller screens.

Current question

Result

The recommended test or model appears after the last required question has been answered.

This application requires JavaScript to render the interactive decision tree.

Source Code

Statistical Decision Tree Repository

Source code on GitHub for the current browser-based Statistical Decision Tree and its interactive decision support logic.

GitHub

Citation And Disclaimer

Prof. Dr. Valentin Schwind. Hochschule der Medien Stuttgart. No liability for external links, correctness, completeness and up-to-dateness of any content. Site visits might result in storing anonymized data (date, time, page viewed). Utilization at the user's own risk. Data can be stored on the computers to facilitate the user's website access. Contribute here.

Find/cite the publication of the toolkit here:


                                Valentin Schwind, Stefan Resch, and Jessica Sehrt. 2023. The HCI User Studies Toolkit: Supporting Study Designing and Planning for Undergraduates and Novice Researchers in Human-Computer Interaction. In Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems (CHI EA '23), April 23-28, 2023, Hamburg, Germany. ACM, New York, NY, USA, 7 pages.
                                https://doi.org/10.1145/3544549.3585890