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
Applied across 6 distinct organisational hypotheses, the framework correctly identified cases where apparent relationships did not survive rigorous testing — including a pricing comparison where the null was retained (p = 0.15) and a marketing engagement analysis where Shapiro-Wilk tests (p < 0.001 for both groups) ruled out parametric methods entirely, triggering Mann-Whitney U and Wilcoxon signed-rank alternatives. Where nulls were rejected, effect sizes were calculated to assess practical significance alongside statistical significance.
Power analysis was conducted prospectively for the key tests, confirming adequate sample sizes to detect effects of the minimum practically meaningful magnitude. This matters because underpowered tests that fail to reject the null prove nothing — they are as misleading as spurious significant results, and far more common in practice.