PESTEL Analysis: Effect-Size-First Inference Readout for Statistical Software
A module for statistics tools (jamovi, JASP, R/Python) that renders every hypothesis test as an effect-size estimate with an uncertainty interval and a practical-significance threshold band, demoting the p-value to a secondary marker so results are read as 'how big' rather than 'significant or not'.
The PESTEL analysis reveals a strong alignment with current academic and professional shifts toward 'practical significance,' though it faces a steep uphill battle against deeply entrenched legacy software habits. While technologically simple to implement, the primary risk is the social inertia of the 'p-value culture' in peer-reviewed publishing and regulatory environments.
Political2
Economic2
Social2
Technological2
Environmental1
Legal2
The idea is driven by a social and professional shift in scientific standards regarding p-values and statistical reporting practices. · Generated 2026-10-10 by cavi/gemma4-31b-it-awq-4bit-32kAI-generated