Standard accuracy metrics in data cleaning actually destroy the statistical integrity of a dataset by removing natural noise.
Data scientists spend massive amounts of time trying to minimize Mean Squared Error when filling in missing values. This research proves that perfect point estimates create systematic biases in variance and correlation for all future analysis. Adding random noise back into the data, known as stochastic imputation, is actually necessary to keep the results honest. Most industry pipelines currently prioritize the lowest error rate possible, which inadvertently masks the true relationships between variables. This means that many AI models are being trained on clean data that is fundamentally lying about the real world. Practitioners need to stop chasing accuracy and start prioritizing the preservation of statistical distribution.
Predicting missing values: A good idea?
arXiv · 2605.03733
Minimizing the Mean Squared Error (MSE) is a key objective in machine learning and is commonly used for imputing missing values. While this approach provides accurate point estimates, it introduces systematic biases in downstream analyses. These biases affect key parameters such as variance, prevalence, correlation, slope, and explained variance. The root cause is that imputed values optimized for MSE are averages, which reduce the natural variability in the data.This paper demonstrates that add