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November 11, 2025Open Access

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

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RMRoccetti Marco

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Implication

Exploratory analysis reveals that adherence to descriptive statistics improves epidemiological models, indicating selection bias can distort outcomes.

Key Points

Multiple statistically irreconcilable paradoxes were found in the source data for vaccine outcomes.
Using standard descriptive statistics revealed implausible incidence rates and significant group imbalances.
Analysis employed basic descriptive and inferential statistics alongside national benchmarks for a recent cohort study.
Findings highlight that advanced modelling in health data requires foundational methodological integrity.

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Roccetti Marco (2025) studied this question.

synapsesocial.com/papers/69252ea3c0ce034ddc3568f7 https://doi.org/10.5281/zenodo.17582658

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Implication

Exploratory analysis reveals that adherence to descriptive statistics improves epidemiological models, indicating selection bias can distort outcomes.

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

Discussion

Member takes

Implication

Key Points

Cite This Study

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

Discussion

Member takes

Implication

Key Points

Cite This Study

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