distribution-profiler

Data profiling requires a numeric column from raw data, derived metrics, or per-user aggregates to analyze distribution characteristics. Providing a clean numeric dataset ensures the diagnostic report accurately identifies skew, modality, and zero-inflation for test selection.

For non-normal data with skew or zero-inflation, the profiler evaluates distribution diagnostics and explicitly recommends appropriate statistical tests while providing caveats and suitable data transformations to guide robust analysis.

To profile discrete versus continuous data distributions, the profiler distinguishes the data type and applies appropriate aggregation level checks to surface modality, zero-inflation, and normality test guidance for accurate experimental design.

The best way to select appropriate statistical tests is to run a distribution profile that identifies normality and bimodality, delivering a structured diagnostic report and an A/B testing playbook tailored to your specific data characteristics.

Yes, you can use data profiling diagnostics for per-user aggregates in experimental design. The profiler applies checks at the appropriate aggregation level and produces an A/B testing playbook to guide robust experimental setup and analysis.