THE QUESTIONS
Sound familiar?
The recurring analytical questions in your field — and the methods that answer them.
Which test fits this design — and does my data meet its assumptions?
Reviewers want effect sizes, CIs, and post-hoc tests — all APA-formatted.
The analysis has to be reproducible in R or Python.
Teaching students to read output, not just produce it.
METHODS THAT FIT
The analyses your work runs on.
Drawn straight from the Statistical & Model Lab catalog — every method with assumption checks and plain-language interpretation.
Group comparison
t-tests to factorial designs, parametric and non-parametric.
Relationships & prediction
Model how variables relate and predict outcomes.
Structure & measurement
Latent constructs, scales, and reliability.
Survival & meta-analysis
Time-to-event and evidence synthesis.
SURVEY LAB
Collect the data too.
Design, distribute, and analyze surveys — the results flow straight into the same analysis engine.
HOW IT WORKS
From data to decision in four steps.
The same guided flow for every method — no coding, no guesswork.
Bring your data
Drop in CSV or Excel, or pull from a public dataset. Types are auto-detected.
Pick the method
Describe your question — the AI recommends fitting methods straight from the catalog.
Run with checks
Assumptions are validated before every run, with plain-language explanations.
Report & export
Get a publication-ready report with R / Python code and APA tables.
EXPLORE THE LABS
Where the work gets done.
Each lab is purpose-built for a different type of analysis — same interface, different lens.
Bring your data. Get answers.
A publication-ready report in minutes — with reasoning, R/Python code, and an AI tutor.