In recent years, rapid technological advancements have led to an unprecedented surge in information availability. While data and analytics offer powerful tools for decision-making, improper handling can result in misinterpretations and biases, turning potential profits into losses. The shortage of data skills along with overconfidence in the accuracy of numbers, increases the risk of errors as quickly as the innovation they stem from.

This article addresses the problem by examining the impact of sampling bias on a mock toy company, ‘Giocattolo’. The firm relies on a Data Provider for retail sales across Europe, yet several flaws (such as missing prices due to privacy reasons, incompatible product categorisations, and sampling bias due to limited coverage) go unnoticed by untrained personnel. A model simulation on raw data showcases how the unrepresentative retail sample, that only included high-revenue stores, results in an overoptimistic total revenue and the consequent six-figure net error. In fact, the naive estimation methods can inflate revenue projections by as much as 30%, resulting in significant misinterpretation. Uncertainty bands are proposed as a tool to convey the uncertainty inherent in biased estimates, offering a more informed basis for decision-making.

In an era where data is abundant, but expertise is scarce, the true advantage lies not in the information itself, but in the ability to interpret it correctly. Learning from a real-life example, this article highlights common oversights in data analysis and the necessity for proper statistical interpretation when research is the ambition.