**Short version:** the data is structurally clean, with no duplicate keys, inconsistent formats or mixed units. The real problems are missing values, a few implausible combinations, and ceiling/floor pile-ups. **Clean (checked, nothing found)** - `Student_ID` is unique: 4,500 rows and 4,500 distinct IDs. Every ID is 11 characters, running from STU_2024000 to STU_2028499. - Categorical columns have no nulls and no spelling variants. Gender, level, platform, device, late-night flag, comparison frequency and impact are all fully populated with a small set of clean labels. - There are no mixed units. Hours columns, 1–5 sleep quality, 0–40 stress and 32–98 mental-health ranges are all internally consistent. - Time budgets are consistent. No row has daily usage plus sleep above 20 hours (0 rows). - Age is plausible at 15–26. No postgraduate is 20 or younger, and no undergraduate is 16 or younger. **Issues found** 1. **Missing values in two measures.** - `Academic_Performance_GPA` is null in 85 rows (1.9%). - `Perceived_Stress_Score` is null in 46 rows (1.0%). - Every other column is complete. Averages or models on these two columns silently drop those rows. 2. **Age and academic level conflict.** 156 rows are High School students aged 19 or older. That is about 3.5% of all rows and about 10.6% of the 1,477 High School rows. This is a possible mislabel or an unusual cohort, so check it before segmenting by level. 3. **Ceiling and floor pile-ups, which may be capping or clipping.** - GPA is exactly 4.0 in 353 rows (about 8% of the table), against a minimum of 1.9. - Daily usage is exactly 14.0 hours in 55 rows, which is the maximum. - Sleep sits at the 3.0-hour minimum in 11 rows and the 10.5-hour maximum in 1 row. - Stress is exactly 0 in 87 rows and exactly 40 in 8 rows, so the scale is used at both extremes. 4. **Extreme but possible usage.** 126 students report 12 or more daily hours, against a mean of about 5.3 and a standard deviation of about 2.6. These are legitimate high-end outliers, not impossible values, but they are heavy tails for averages. 5. **Skewed and small categories, which affect reliability rather than correctness.** - 82% of rows are labelled Beneficial and only 165 are Negative. - Postgraduate has 246 rows. - LinkedIn has 96 rows. - "Prefer not to say" has 93 rows. - Subgroup comparisons on these will be noisy. **Caveat:** one check column in my profiling query (`odd_id_format`) returned 4,500. Given that every ID has the same 11-character length and range, I read that as a poorly specified check, not as evidence of bad IDs. I did not test for a distribution-based outlier rule, such as an IQR cutoff, beyond the min/max and threshold checks above. **Suggested handling:** exclude or flag the null GPA and stress rows per analysis, review the 156 older High School rows, and treat the GPA 4.0 and usage 14.0 pile-ups as possibly capped values.