In modern scientific inquiry and data-driven decision-making, statistical significance occupies a central role. Researchers, practitioners, and policymakers often rely on p-values and hypothesis tests to distinguish genuine effects from random noise. However, misapplications of statistical significance—especially in contexts involving multiple hypothesis testing—can lead to inflated false-positive rates, misguided conclusions, and wasted resources. This article explores
Introduction A/B testing has become the gold standard for data-driven decision-making in digital products, marketing campaigns, and business optimization. However, beneath the seemingly straightforward concept of comparing two variants lies a complex web of potential pitfalls that can render experiments completely invalid. Configuration errors in A/B tests are not merely inconveniences—they can lead to catastrophically
Abstract:Confidence Interval Neglect (CIN) – the cognitive bias of underweighting or completely ignoring the uncertainty represented by confidence intervals (CIs) in favor of point estimates – is a pervasive and costly flaw in performance reporting across finance, technology, healthcare, science, and policy. This comprehensive analysis explores the psychological roots, widespread manifestations, severe consequences, and potential