A data analyst is cleaning a dataset of 10,000 patient records for diabetes forecasting. She finds that 8% of the records contain missing HbA1c values, 4% have inconsistent glucose readings, and 2% have both errors. What percentage of records have only one type of error?

Wed, 06 May 2026 05:37:14 +0000

**Why Precision in Data Cleaning Matters: Uncovering Hidden Errors in Diabetes Forecasting**
In an era where reliable health data drives life-saving forecasts, a data analyst working on a 10,000-record diabetes dataset recently uncovered a subtle but critical pattern: errors quietly undermine data quality. With 8% missing HbA1c readings and 4% showing inconsistent glucose values—plus 2% bearing both issues—questions arise about how such gaps affect predictive models and patient outcomes. This isn’t just a technical detail; in a digital health landscape increasingly shaping care delivery, understanding these nuances ensures smarter insights and more accurate analytics. **Why This Data Cleanup Detail Is Gaining Attention in the U.S.**
Across healthcare technology and data science communities, the quality of patient data is under rising scrutiny. Recent industry reports highlight that even small error rates can significantly distort forecasts—especially in predictive models used for early diabetes risk detection. With over 10 million Americans living with diabetes or pre-diabetes, even minor inaccuracies in datasets like the one described can ripple through algorithms, potentially impacting treatment planning and resource allocation. As healthcare providers and tech developers prioritize data integrity, identifying and addressing these layered errors has become a crucial step toward reliable digital health innovation. **How A Data Analyst Is Addressing Dual Errors in a Diabetes Dataset**
Analyzing a 10,000-patient dataset gave a data analyst a clearer picture of data reliability. Of the records examined, 8% show incomplete HbA1c values—key markers for long-term blood sugar control—while 4% contain inconsistent glucose readings, such as values that deviate from expected ranges. Critically, 2% exhibit both missing HbA1c and inconsistent glucose data. These overlapping errors are not random; they reflect real challenges in clinical data capture across different testing waves or external sources. Understanding these patterns is essential, not only for cleaning the current dataset but also for improving future data collection workflows. To determine how many records contain only one type of error, we analyze the distribution carefully:
- Missing HbA1c: 8%
- Inconsistent glucose readings: 4%
- Both errors: 2%
Using set theory logic, the percentage with only one error is calculated as:
(8% – 2%) + (4% – 2%) = 6% + 2% = **