SWOT 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 SWOT analysis reveals a strong alignment between expert user needs and the proposed design, leveraging a clear gap in current software UX. While the technical implementation is straightforward, the primary challenge is the deeply ingrained cultural reliance on p-values in academic and regulatory environments.
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As an early-stage innovation, it is essential to map the internal technical strengths against the external opportunity to disrupt standard software outputs. · Generated 2026-10-10 by cavi/gemma4-31b-it-awq-4bit-32kAI-generated