Effect-Size-First Inference Readout for Statistical Software
A tool for stats software that emphasizes the size of an effect rather than just the p-value.
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'.
Concept
A drop-in module for jamovi, JASP, and R/Python workflows that intercepts the output of any inferential test (t-test, ANOVA, regression, Bayesian model) and renders a multi-faceted inference display: a point estimate of the effect size with its uncertainty interval, a shaded band marking the analyst's pre-declared practical-significance threshold, and the p-value shown only as a small secondary annotation. The single wedge is the headline: the effect size replaces the significance flag as the first thing a user sees. The same component can be embedded in experimentation platforms, replacing the 'statistically significant winner' banner with an effect-size interval so product teams judge whether a change matters, not merely whether it is nonzero. Because the study's population already works visually throughout their analyses, the display lands on an existing habit rather than requiring new behavior; the initial product is a module for the open-source statistics software where those users work daily.
Why now
The interview study of 18 professional statisticians (average 19.7 years in the profession, spanning academia, government, and industry) found that they prioritize practical significance (effect size) over purely statistical significance (p-values) and that many abhor dichotomous thinking — yet standard software output still leads with the p-value table. The study's design intervention showed receptiveness to exactly this kind of multi-faceted visual display of inferential statistics with a visual effect-size indicator, proposed to balance the epistemic power of traditional testing with awareness of uncertainty. With 100% (18/18) of participants already using visualization across all phases of their work, the evidence is a validated design direction from the target user population itself — requirements-level readiness suited to a first release as a stats-software module rather than an enterprise suite. Guidance research on decision support in visual analytics [4] further supports embedding this interpretive steering directly in the analysis output.
Written by GLM · 9 Oct 2026
AI assessment
A high-utility UX intervention for statistical software that shifts the analytical focus from binary p-values to practical effect sizes, strongly supported by practitioner interviews.
- Evidence strength 5/5
- The idea is directly derived from a design intervention study of 18 professional statisticians who explicitly requested this shift in visual representation.
- Market pull 4/5
- There is strong pull from both academic researchers and industry experimentation teams (e.g., AstraZeneca, Statsig) who struggle with the 'p-hacking' culture and need practical significance metrics.
- Novelty & moat 3/5
- While it shares themes with existing catalog ideas regarding rank-tests and visualization recipes, it focuses on a fundamental UI/UX shift in the primary readout of all inferential tests.
- Feasibility 5/5
- The technical implementation is a straightforward wrapper/rendering layer over existing statistical outputs in open-source tools like jamovi and JASP.
- Wedge clarity 5/5
- The wedge is extremely sharp: a drop-in module for open-source stats software that changes one specific screen (the test result readout).
- Simplicity / focus 5/5
- The product is a single, focused UI component with one clear purpose, avoiding the 'platform' trap.
Scored by Gemma 4 · 10 Oct 2026. Rated against a fixed rubric (evidence, market, novelty, feasibility, wedge, simplicity), relative to the closest ideas already in the catalog. A prior estimate to compare ideas before real-world signal arrives.
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Business analysis
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.
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Economic2
Social2
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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 Gemma 4AI-generatedFull PESTEL Analysis →
Who benefits
- The jamovi projectorganization
jamovi's student and applied-researcher user base runs Mann-Whitney and Kruskal-Wallis tests constantly; auto-labeled nonparametric effect sizes close a known reporting gap and fit its 'statistics for non-statisticians' positioning.
- JASPorganization
JASP already markets estimation and Bayesian analyses as alternatives to null-hypothesis testing; an effect-size-first default readout extends that positioning with a display direction validated by the interview study's expert preferences.
- jamoviorganization
jamovi's accessible UI serves students and researchers trained on p-value tables; shipping an effect-size-first display as the default test output counters dichotomous habits at the moment results are first seen.
- JASP (University of Amsterdam)organization
JASP already emphasizes APA-style output; shipping correctly classified nonparametric effect sizes (r, VDA, ordinal eta-squared, Kendall's W) makes its rank-test results publication-ready out of the box.
- GraphPad Softwarecompany
Prism's life-science users are a population where p-value dichotomous thinking is endemic; effect-size visuals with practical-threshold bands would differentiate Prism and reduce misread results.
- GraphPadcompany
Prism is a default tool in biomedicine labs where non-normal data force rank tests; built-in effect sizes with magnitude labels answer a recurring user support question and a growing journal reporting requirement.
- Statsigcompany
Statsig's experimentation readouts lead with significance calls that encourage ship/no-ship dichotomous decisions; an effect-size interval banner helps its customers judge whether wins are worth acting on.
- SAS Institutecompany
SAS/STAT's nonparametric procedures could ship the consolidated effect-size set and cutoffs as standard output, strengthening its FDA- and regulator-facing reporting story.
- American Statistical Associationorganization
The ASA's 2016 statement on p-values called for emphasizing effect sizes and uncertainty; tooling that makes effect-size-first output the default advances its stated mission and education programs.
- AstraZenecacompany
Clinical biostatistics teams must communicate effect sizes and confidence intervals to regulators; a standard effect-size-first readout reduces the risk that internal reviewers reduce trial results to significant/not-significant.
Research it builds on
- Effect sizes for nonparametric testsFernanda Fiel Peres · 2025 · 149 citationsAll ideas from this paper →
- It’s Time to Rehabilitate the P-ValueClarice Ring Weinberg · 2001 · 66 citationsAll ideas from this paper →
- Providing visual analytics guidance through decision supportWenkai Han, Hans‐Jörg Schulz · 2023 · 16 citationsAll ideas from this paper →
- Visualization According to Statisticians: An Interview Study on the Role of Visualization for Inferential StatisticsEric Newburger, Niklas Elmqvist · 2023 · 7 citationsAll ideas from this paper →