### Fit-for-Purpose Verdict **Verdict:** Fit for single-table time-series analysis. Not fit for any analysis that spans tables. **What it supports well** - **Time-series analysis on temporal columns.** `bedtime_screentime_sleep_debt` (8,500 rows) is the only table in scope and carries the temporal columns this work depends on. With completeness at 100%, trend, seasonality, and period-over-period comparisons will not be distorted by missing values. **What it cannot support, and why** - **Cross-table aggregation.** No validated joins were detected, and the dataset contains only this one table. There is nothing to aggregate across, so any cross-table figure would be unsupported. - **Integrity assurance.** The 100% referential integrity score is vacuous: with no foreign key relationships detected, there are no constraints that could be violated. It does not show that relationships were checked and held. The same applies to the 100% largest-table share, which follows from there being one table. **Top remediation steps** 1. **Report referential integrity as not applicable, not as a pass.** No FK relationships were detected, so the 100% score is a vacuous result. Presenting it as evidence of relational quality would overstate what was tested. 2. **Scope deliverables to the single table.** Build time-series outputs on the temporal columns of `bedtime_screentime_sleep_debt` and keep conclusions within that table's boundaries. Do not publish cross-table aggregates from this dataset. 3. **Validate joins before extending the dataset.** If other tables are added later, confirm their joins before aggregating across them. This is not a priority for the current dataset.