Correcting for age-specific and trend biases in data: STL decomposition as a tool for enhancing the accuracy of data-driven journalism investigations

Abstract

This article presents a method for correcting systematic errors in time series to enhance data-driven journalism. Using birth rate seasonality analysis, it applies STL-decomposition to remove long-term trends and age-related shifts. The study shows that raw data distort seasonal patterns, while the corrected approach reveals fluctuations linked to sociocultural factors, underscoring the need for statistical adjustment in media data. The method's utility is proven with real journalistic cases, including social issue analysis and verification of official statistics.