Inspect the data before modeling
Missing values, outliers, inconsistent units, wildly unbalanced group sizes. Left unaddressed, these make an elegant model confidently wrong.
AI Empirical Data Analysis
Move from data checks and statistical models to charts and explanations with a reproducible analysis trail.
The usual worry about letting an AI analyze data is the same everywhere: it produced a number, and there is no way to tell where the number came from or whether it is right. A conclusion you cannot trace is a conclusion you cannot put in a paper or a report.
The research agent handles this by writing the analysis as code and running it. It writes a script, executes it against your data, reads the output, spots problems, fixes the script, and repeats until it runs clean. You receive the conclusion along with the code, intermediate output, and charts, so any step can be rerun and checked. That is the substantive difference from asking a model to work something out in prose: there the number is generated, here it is computed.
What it can do
Numbers are computed, not written
Every statistic comes from code that actually executed, not from the model recalling what such a figure usually looks like. Scripts and run logs are delivered alongside the results.
It notices its own failures
When a script errors or the output is implausible, it reads the traceback, locates the cause, edits, and reruns rather than submitting a broken result as the answer.
Publication-ready figures
Axes, legends, units, and error bars are present, so charts can go straight into a paper or report without a formatting pass.
How it works
- Data health checkMissingness and outliers are quantified, variable types and units verified, and any problems reported to you together with proposed handling.
- Choose the approachA method is selected for your actual research question, with an explanation of why it fits, what it assumes, and where it would not apply.
- Write, run, self-correctScript, execute, read the result. Errors and implausible output send it back to edit and rerun; that loop closes without you driving it.
- Visualize and interpretCharts are produced along with an interpretation that separates well-supported conclusions from patterns that are only suggestive.
Worked examples
Group comparison in survey data
Here is my survey data. I want to know whether the three groups differ on these scales. You decide which test is appropriate.
A data quality report flagging two outliers, then the rationale for the chosen test, the results, post-hoc comparisons, and a publication-ready group comparison figure.
Finding drivers in business data
From this sales data, identify the main factors affecting conversion, and be clear about what is correlation and what can be argued as influence.
An analysis covering variable selection, model results, and robustness checks, explicitly separating statistical association from defensible influence, with full code attached.
Frequently asked questions
How is this different from just asking a chatbot to compute it?
Where the number comes from. Asked in prose, a model produces a plausible-looking figure from language probability. Here code runs against your real data, and you get the code and intermediate output to rerun yourself.
Which data formats are supported?
Common tabular formats including Excel and CSV, and multiple tables can be uploaded together for joined analysis. Large datasets are processed in chunks.
Can the results go straight into a paper?
Charts and statistics can be used directly and the code works as supplementary material. Whether the method suits your research design, and how the conclusions are worded, remains your call.
Is my data used to train models?
No. Uploaded data is used only to complete the analysis you requested.