Get a clear statistical method recommendation
for your exact study design — not a generic 'it depends'
When you ask AI which test to use, the answer is usually vague — 'it depends on the distribution' — without explaining how to actually decide. Real medical research needs a recommendation grounded in your specific outcome type, group structure, and sample size.
Statistical Method Advisor (Lite) turns a real statistics consultation into a structured AI workflow. Describe your data, get a primary recommendation with rationale, alternatives with assumption checks, and a ready-to-adapt Methods sentence — no setup needed.
Use this AI tool→ Fill in variables and run directly with AI. Free to try.
Fill Variables
e.g., Cardiology, Oncology, Public Health, Psychiatry, Emergency Medicine
List each variable with name, type, and range. e.g.: Independent: treatment group (categorical: Drug A / Drug B, independent); Dependent: systolic BP change (continuous, mmHg); n = 45 per group
e.g., Does Drug A reduce systolic blood pressure more than Drug B in hypertensive patients?
e.g., 简体中文, Spanish, 日本語, English
Same question, two ways to ask
The real difference is whether AI clarifies the data characteristics that drive the decision before naming a method.
What you send
What statistical method should I use to compare postoperative length of stay between two groups?
Typical result
- AI may mention both t-test and Mann-Whitney U without telling you how to choose - It may skip whether groups are independent, whether length of stay is skewed, and whether sample size is small - It may not tell you what to do if assumptions fail You still have to reconstruct the decision process yourself.
Variables you fill in
Clinical field: Critical care Data description: Outcome = postoperative length of stay (continuous, clearly right-skewed); two independent groups; exposure = early mobilization vs usual care; about 45 patients per group Research question: compare length of stay between groups Output language: English
Structured output you get back
1. Data characteristics confirmation - Continuous outcome, 2 independent groups, medium sample size 2. Recommended method - Primary: Mann-Whitney U test - Why: length of stay is often skewed, so median/IQR is more robust 3. Alternative option - If distribution is close to normal and variance is acceptable, use an independent-samples t-test 4. Interpretation and Methods draft - Reminds you to report effect size, significance meaning, and manuscript wording
How this tool works
You fill in the study details. The consultation logic is already built in.
You fill in
- Clinical field: Tell the AI whether this is cardiology, oncology, ICU, or another area so it can stay aligned with common reporting norms in that field.
- Data description: Describe the outcome, predictors, number of groups, whether samples are paired, sample size, and any known distribution or missing-data pattern.
- Research question: State whether you want to compare groups, assess association, evaluate predictors, or analyze a time-to-event outcome.
- Output language: Get the recommendation, explanation, and draft Methods sentence in the language you actually use for discussion or reporting.
Already built in
- If critical information is missing, the AI is told to ask before assuming distribution, pairing, or sample structure
- If multiple methods are reasonable, it presents a primary recommendation and honest alternatives with selection criteria
- Each method comes with key assumptions and concrete ways to check them, such as Shapiro-Wilk, Levene, or Q-Q plots
- It does not stop at naming a test; it also explains which result to focus on and what “not significant” does not mean
- It gives you a ready-to-adapt Methods sentence so the recommendation can move straight into a manuscript draft
What one consultation gives you
These sections come directly from the real statistical-method-advisor-lite tool structure.
Data confirmation
It restates outcome type, predictors, group structure, paired status, and sample size so the recommendation is not built on a misunderstanding.
Primary recommendation
It tells you which method to use first and explains, in plain language, why that method fits your data.
Alternatives and assumption checks
It gives backup options, tells you when to switch, and shows how to check normality, variance, or other key assumptions.
Interpretation and Methods wording
It points out which statistic matters most, warns against common misreadings, and gives you a ready-to-edit Methods sentence.
How to use it
Describe the data properly
Include variable types, number of groups, paired vs independent structure, sample size, and any known distribution pattern. Better input produces a more usable recommendation.
Click AI Run
AI Run opens a chat. The assistant will first confirm what it understood about your data structure before recommending a method.
Use it to decide the analysis
Check the stated assumptions first, then finalize the method. If the problem becomes multivariable, longitudinal, or survival-based, move to a more complete workflow.
Enter your field, data description, and research question to get a primary method recommendation, alternatives, assumption checks, interpretation guidance, and a draft Methods sentence.
→ Run directly with AI. Free to try.
FAQ
What is this tool best for?
It is best for common univariate or bivariate decisions, such as comparing groups, analyzing binary outcomes, checking associations, or making a first pass on a time-to-event question. It helps you structure the decision before you need a full statistics consult.
What if my data are not normally distributed?
This tool tells the AI to explain how to check normality and variance assumptions and what alternatives to use if those assumptions fail. For obviously skewed outcomes like length of stay or cost, nonparametric options are often more appropriate.
Will it generate full R, Python, or SPSS code?
The Lite version focuses on method selection, rationale, and simple command hints rather than full production-ready scripts. If you need executable code or more advanced models, you should move into a fuller analysis workflow.
Should I paste real patient-level data into AI?
Usually no. A structural description is safer and is often enough: outcome type, group structure, approximate sample size, and distribution pattern. Avoid identifiable information or raw patient rows.
Can it handle multivariable regression, repeated measures, or survival analysis?
It can help with an initial direction, but the Lite version is not designed to fully solve complex modeling choices. If you already know the problem involves multivariable regression, longitudinal clustering, or survival methods, use the more complete statistics workflow.