**The most important numeric metric is `total_sleep_hours`.** I chose it on judgment, not by a computed ranking. The dataset's `sleep_debt_category` is derived from it, and the likely decisions here (cutting phone time, caffeine cutoffs, filters, fatigue modelling) all concern sleep duration and sleep debt as the outcome. Other measures such as sleep latency, deep and REM sleep, and next-day fatigue behave more like related outcomes or drivers. **Highest records: 9.8 hours.** The rendered result shows five users at this maximum: USR-01351, USR-01363, USR-01396, USR-01715 and USR-02117. - All five are in the "Optimal Recovery" category. - All five have a next-day fatigue score of 1.0, the best value in this sample. - Four of the five are Morning Larks and the other is Intermediate. Four of them report 0 mg of caffeine after 5pm, and the fifth reports 79 mg. - Occupations are mixed: Corporate 9-to-5, Remote Tech and Student. - Sleep latency is relatively short, 16–34 minutes. **Lowest records: 3.2 hours.** The five users shown are USR-00001, USR-00003, USR-00025, USR-00070 and USR-00086. - All five are Healthcare / Shift Workers, and four of the five are Night Owls. - Sleep latency is long, 48–84 minutes, compared with 16–34 minutes in the top group. - Fatigue scores are high, 8.0–10.0, against 1.0 in the top group. **Caveats** - Both extremes are ties, so only five rows per end were shown. There may be more users at 9.8 or 3.2 hours than these. - At exactly 3.2 hours, USR-00001, USR-00003 and USR-00070 are labelled "Severe Sleep Debt", while USR-00025 and USR-00086 are labelled "Moderate Debt". So `sleep_debt_category` is not a pure function of `total_sleep_hours`, despite what the dataset card suggests. Another field may feed into it, or the rule is inconsistent. This is worth checking before using the category as a label. - The data is observational, so the contrast between the short-sleep and long-sleep groups (occupation, chronotype, latency, fatigue) shows association, not cause.