Reading is_returning before trusting it in ab-test-checkout

Profiling is_returning on ab-test-checkout before anyone builds anything on top of it.

6,000 rows, no nulls, 2 distinct values. Whether the user had visited before exposure.

It is true in 42.1% of rows — 2,524 of 6,000.

That imbalance is the single most important fact about the column. It sets the baseline any model has to beat, it decides whether a per-segment breakdown will have enough rows in the minority class to say anything, and it determines how wide the confidence interval on any rate computed from it will be.

select
  count(*)                       as rows,
  count(is_returning)            as present,
  count(*) - count(is_returning) as nulls,
  count(distinct is_returning)   as distinct_values
from ab_test_checkout;

A near-balanced flag, which is more pleasant to work with than most. Confirm the balance rather than assuming it — it is the exception, not the rule.

Where this bites: slicing by a dimension with 12 levels leaves roughly 210 true rows per slice on average. That is thin enough that the noisiest segment will look like the most extreme one, every time, and someone will read the ranking as a finding.

The grain is one row per exposed user, which is the context every one of those numbers depends on. None of them survive a change of grain, which is why "profile the column" and "profile the table" are the same job. Full schema, and the CSV, on the dataset page.