Data shape

Before and after, same subject

One row per customer is a different problem from one row per customer per month. Shape decides which methods can apply to your file at all.

How we actually do it

Shape is detected from your file. Column types, repeat keys and date columns decide whether you have a cross-section, a panel or a duration.
The wrong shape is refused, not fudged. If your data can't support the analysis you picked, you get told which analysis it can support.
Repeated measurements are respected. Multiple rows per subject are modelled as such instead of being averaged into a single misleading number.

Paired Comparison — Before vs After — sample report Open full report →

That is the actual report this analysis produces — not a mock-up. Every analysis on this page delivers the same shape of output on your own data.

Every analysis classified under this data shape.

Did things change before vs after?