Model selection
Answer guided questions about your goal and data to narrow the candidate set.
Find a model Evidence-aware model selection
Choose the right statistical model—with reasons you can defend.
Translate your research question and data structure into a focused recommendation, practical alternatives, assumptions, diagnostics, and implementation guidance.
No statistical jargon required
Assumptions made visible
Python and R examples
44documented models
24/7self-guided access
5+implementation ecosystems
A clearer analytical workflow
Support for the decisions around the model—not just its name.
Move from an initial question to a method you can explain, implement, check, and communicate to collaborators.
Model library
Compare assumptions, use cases, variable types, code, and interpretation guidance.
Explore modelsQuestionnaire design
Build research instruments that align measures with the intended analysis.
Design a questionnaireExpert review
Bring in a statistician when design, assumptions, or interpretation need deeper judgment.
Find an expertTransparent criteria
Recommendations connect directly to the characteristics you provide.
Alternatives included
See other compatible approaches instead of a false single-answer result.
Validation first
Use assumptions and diagnostics before drawing substantive conclusions.
Start with your research question
Get a focused recommendation in a few guided steps.
You do not need to know the model name in advance. Describe what you want to learn and what your data look like.
Start model selection