The data is fairly clean. I found no impossible values or mixed units, but there are missing values, a few suspicious clusters, and some ceiling effects to treat with care. **Clean checks (no problem found)** - The table has 4,500 rows and 4,500 distinct Student_IDs, with no nulls in the ID. Every ID is 11 characters long, and the range runs from STU_2024000 to STU_2028499. My regex-based "odd format" check flagged all 4,500 rows, which is almost certainly a bug in my pattern, so I treat that flag as unreliable. The length and range results show a consistent format. - All numeric fields are in plausible ranges: | Field | Range | |---|---| | Age | 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 | | GPA | 1.9–4.0 | - No row has daily usage plus sleep above 20 hours. - No postgraduates are aged 20 or under, and no undergraduates are aged 16 or under. - The categorical columns (Gender, Academic_Level, Platform, Device, Comparison frequency, Late_Night_Usage, Overall_Impact) have no nulls and no spelling or casing variants. The category lists in step 1 are tidy. I found no sign of mixed units. **Issues and oddities** 1. **Missing values.** Perceived_Stress_Score has 46 nulls (4,454 of 4,500 filled). Academic_Performance_GPA has 85 nulls (4,415 filled). Both are small, at about 1% and 1.9%, but any analysis using these two measures will silently drop those rows. 2. **Ceiling and floor clustering (possible capping or clipping).** - 353 students (about 8%) have a GPA of exactly 4.0, which suggests a capped scale and a ceiling effect. - Daily usage is exactly 14.0 for 55 students, which looks like a cap. - 11 students sleep exactly 3.0 hours and 1 sleeps exactly 10.5 hours, the two extremes of sleep duration. - Perceived stress is 0 for 87 students and 40 for 8. Zero on a 0–40 stress scale is rare in real data, so some of those may be placeholders or defaults. 3. **Age and academic level mismatch.** 156 High School students are aged 19 or older. That is possible (late graduation, gap years) but worth verifying rather than assuming it is correct. 4. **Extreme usage.** 126 students report 12 or more hours of daily use. That is plausible but extreme, and these rows will have high leverage in any correlation or regression. 5. **Imbalanced categories** (a skew, not an error): - Overall_Impact is 3,681 Beneficial, 654 Neutral and 165 Negative. - Smartphone accounts for 3,775 of 4,500 devices. - Postgraduates number only 246. - Non-Binary (122) and "Prefer not to say" (93) are small groups, so per-group averages for them will be noisy. 6. **Row-level uniformity.** The data reads as organic, but the large Beneficial share of Overall_Impact and the clipped values suggest the scores may be self-reported or generated with bounds. The Mental_Health_Index scale and direction are not documented, so check them before interpreting. **Recommendations** - Treat the exact 4.0 GPA, 14.0 usage and 0 stress values as censored, not as ordinary observations. - Decide how to handle the 46 and 85 nulls before comparing groups. - Flag the 156 High School students aged 19 or older and the 126 students at 12 or more hours for sensitivity checks. The SQL results behind these figures are the profile and category-count queries (steps 0–2).