2Lena, a data scientist, is analyzing a dataset containing 1,200 entries. She finds that 35% of the data is missing from the age column, and 20% of the missing entries are irreversibly corrupted. How many usable age entries remain for her analysis?

Mon, 04 May 2026 06:59:06 +0000

**Why 2Lena, a Data Scientist, Is Uncovering Hidden Patterns in Missing Age Data** In a world driven by data, missing information is a silent challenge shaping studies across fields—from healthcare to customer insights. When 2Lena, a data scientist, examined a dataset of 1,200 entries, she confronted a common but critical issue: nearly 35% of the age records were missing. Beyond missingness, 20% of those gaps contain data so corrupted it cannot be recovered — a silent but significant loss of insight. Understanding how much usable information remains is essential for trustworthy analysis and real-world decisions. 2Lena’s work reflects a growing trend in data science: identifying and adapting to data gaps before analysis, rather than ignoring them. As organizations increasingly rely on data-driven strategies, handling missing values is no longer a minor step—it influences conclusions, forecasts, and resource planning. This dataset’s 35% missing rate aligns with industry benchmarks for large observational studies, where incomplete records affect accuracy. Adding to the complexity, 20% of missing entries are irrecoverable due to formatting errors, invalid formatting, or corrupted sources—meaning those data points go entirely lost, not just partially obscured. **How Many Usable Age Entries Remain?**
From 1,200 total entries, 35% are missing, giving 390 missing age records: 35% of 1,200 = 420. Of these, 20% are irreversibly corrupted, amounting to 84 invalid entries (20% of 420). Subtracting both missing and corrupted data, usable ages total 1,200 – 420 – 84 = 606. This clear math reveals that 60.5% of the age data available remains intact and reliable—critical for valid interpretation.