Statistical Hypothesis Testing Framework
A rigorous statistical analysis framework demonstrating the application of parametric and non-parametric hypothesis tests to real organisational data, with particular focus on the critical distinction between correlation and causation.
The Challenge
Organisations routinely misinterpret data by conflating correlation with causation, leading to flawed strategic decisions. A marketing team might attribute a sales increase to a recent campaign when seasonal effects or external factors were the actual drivers.
Without rigorous statistical validation, data-driven decisions are built on sand. Hypothesis testing provides the framework to distinguish genuine effects from noise, correlation from causation, and significant findings from statistical artefacts.
Approach
Results
The framework demonstrated that many commonly held assumptions within the organisational data did not survive rigorous statistical scrutiny. Several apparent relationships proved to be correlational rather than causal, and effect sizes for genuine findings were often smaller than intuition suggested, highlighting the value of formal hypothesis testing over informal pattern recognition.