Methodological Precedence in Health Tech: An Exploratory Case on the Necessity of Descriptive and Inferential Statistics to Validate Inputs for ML/Big Data Epidemiological Models

Abstract: The adoption of sophisticated analytical tools, including Machine Learning (ML) and massive data processing, has accelerated health research. However, a foundational principle asserts that the rigor of these complex methods is dependent on the integrity and validity of the underlying statistical design. We posit that advanced analyses, particularly in epidemiology, must be subsequent to the rigorous verification of basic methodological coherence. This study uses an exploratory case to demonstrate a crucial cautionary principle: complex models amplify, rather than correct, severe methodological flaws. To demonstrate this, we apply standard descriptive and inferential statistical methods (Z-tests, Confidence Intervals, and t-tests) alongside established national epidemiological benchmarks to a recently published cohort study on vaccine outcomes and psychiatric events. Through this approach, we expose multiple, statistically irreconcilable paradoxes within the source data, including implausible incidence rates and profound baseline group imbalances. These findings, proven by inferential statistical evidence, demonstrate that the observed effects (e.g., contradictory Hazard Ratios) are not biological but are mathematical artifacts stemming from uncorrected selection and classification biases in the cohort construction.Our analysis serves as a robust demonstration that the validity of any conclusion drawn from subsequent advanced ML or statistical modeling sourced form health data rests entirely on first passing the test of basic epidemiological consistency.