**Overall, the data is structurally clean, with no impossible values and no format problems. The real issues are missing values, clipped values and a few implausible combinations.** **Clean checks (ran, nothing found)** - All 4,500 `Student_ID`s are unique and non-null. Every ID is 11 characters, running from STU_2024000 to STU_2028499, so there are no duplicates and no mixed ID formats. One of my ID-format flags returned 4500, but it is clearly a faulty test: the ID lengths and min/max show a uniform pattern. - The ranges are plausible. Age is 15–26, daily usage 0.9–14.0 h, weekend extra hours 0–4.5, sleep 3.0–10.5 h, sleep quality 1–5, perceived stress 0–40, mental health index 32–98 and GPA 1.9–4.0. Nothing is negative or outside its scale. - Daily usage plus sleep never exceeds 20 hours (0 rows), so there are no physically impossible time budgets. - All categorical columns (gender, level, platform, device, comparison frequency, late-night, impact) have 0 nulls. The category labels are consistent, with no spelling variants. - I found no sign of mixed units. Hours, 1–5 scores and the 0–40 stress scale each behave like a single scale. **Issues found** 1. **Missing values in two measures.** `Perceived_Stress_Score` is null in 46 rows and `Academic_Performance_GPA` in 85 rows (about 1% and 1.9%). Averages must skip these, and the missingness may not be random. 2. **Inconsistent age and academic level.** 156 High School students are aged 19 or older. That is possible but unusual, and it may be a labelling error. The reverse checks were clean: no postgraduates aged 20 or under, and no undergraduates aged 16 or under. 3. **Values piled up at the limits.** This pattern suggests clipping or capping rather than natural data: - 353 students have a GPA of exactly 4.0. - 55 have daily usage of exactly 14.0 h, and 126 report 12 h or more. - 11 have sleep of exactly 3.0 h and 1 has exactly 10.5 h. - 87 have a stress score of 0 and 8 have exactly 40, which are suspicious extremes on that scale. 4. **Very small or imbalanced groups.** This is not an error, but it limits any comparison: - Non-Binary has 122 students and "Prefer not to say" has 93. - Postgraduate has 246 students and Tablet 129. - LinkedIn has 96 and X (Twitter) 206. - Overall_Impact is lopsided: Beneficial 3,681, Neutral 654 and Negative only 165. **Caveats** - The `Weekend_Extra_Hours` and `Daily_Usage_Hours` columns are tagged as "temporal axes" on the dataset card, but they are durations, not dates. There is no real time dimension apart from the ingestion timestamp, so trend analysis is not possible. - The data reads as organic, but the strong clustering at the caps and the 3.0 h sleep floor deserve a check against how the data was collected.