This question evaluates a data scientist's competency in diagnostic analytics, experiment diffing, cohort and identity-resolution analysis, metric instrumentation, and SQL-based quantitative validation.

You observed a sudden pattern: the number of users switching accounts increased, while overall active users decreased. Assume:
Provide a concrete diagnostic plan covering (a)–(f).
(a) List 5–7 plausible hypotheses spanning measurement artifacts, product changes, and behavior shifts.
(b) Specify the exact metrics and breakdowns you will pull (e.g., by client, geo, new vs returning, person_id vs user_id, cohorts, time-of-day).
(c) Describe at least three targeted slice analyses to confirm or refute cross-contamination between accounts (i.e., multiple accounts used by the same person/device).
(d) Outline event/experiment diffing steps to identify recent launches correlated with the shift.
(e) Provide minimal SQL or pseudocode to quantify how many active user_ids map to the same person_id compared with last week.
(f) List immediate mitigations if the root cause is: (i) a logging bug, (ii) authentication friction, or (iii) users gaming policy limits by rotating accounts.
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