Making Effective Statistical Inferences: From Significance Testing to the Open Science Inference Ecosystem (2016-2026)
Pith reviewed 2026-05-15 00:06 UTC · model grok-4.3
The pith
Statistical inference unifies into evidence-centric and decision-centric domains within open science.
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
Core claim
The central claim is that statistical inference has evolved into a unified system consisting of two complementary domains: evidence-centric inference, which quantifies compatibility between data and models, and decision-centric inference, which guides actions under uncertainty, all embedded in transparent open science workflows that include preregistration, Registered Reports, multiverse analysis, and updated standards such as PRISMA 2020 and CONSORT 2025.
What carries the argument
The conceptual unification of statistical inference into evidence-centric inference (quantifying data-model compatibility) and decision-centric inference (guiding actions under uncertainty).
If this is right
- Compatibility-based p-value interpretations and S-values replace binary significance decisions with graded evidence statements.
- Equivalence testing using smallest effect sizes of interest supplies assessments of practical relevance alongside statistical compatibility.
- Bayesian workflows and sequential e-value methods extend evidence quantification beyond single tests.
- Preregistration, Registered Reports, and multiverse analysis embed inference tools inside reproducible workflows.
- Updated reporting standards such as PRISMA 2020 and CONSORT 2025 operationalize the multidimensional evaluation of evidence and relevance.
Where Pith is reading between the lines
- Fields outside statistics, such as clinical research, could adopt the split to separate evidentiary summaries from treatment recommendations more explicitly.
- A practical test of the unification would be whether training programs that teach both domains reduce common misinterpretations of p-values compared with traditional curricula.
- The framework implies that software defaults should shift from significance thresholds to simultaneous displays of compatibility intervals and decision thresholds.
- Long-term adoption might be tracked by measuring changes in the frequency of dichotomous language in published abstracts.
Load-bearing premise
The methodological advances and reforms from 2016 to 2026 form a coherent ecosystem that can be cleanly unified without significant gaps or contradictions.
What would settle it
A systematic survey of published statistical practice after 2026 that finds widespread adoption of the two domains yet persistent irreconcilable contradictions between evidence measures and decision procedures that the framework cannot resolve.
read the original abstract
Statistical inference has undergone a profound transformation over the past decade, evolving from a significance-testing paradigm toward a comprehensive, transparency-driven framework embedded within the broader open science ecosystem. While traditional approaches such as null hypothesis significance testing (NHST) remain widely used, they have been increasingly criticised for fostering dichotomous thinking, misinterpretation, and irreproducible findings. This review synthesises developments from 2016 to 2026, integrating methodological advances-including compatibility-based interpretation of p-values, S-values, equivalence testing with smallest effect sizes of interest (SESOI), Bayesian workflow, and sequential inference using e-values-with systemic reforms such as preregistration, Registered Reports, multiverse analysis, and updated reporting standards (PRISMA 2020, CONSORT 2025). A central contribution of this article is the conceptual unification of statistical inference into two complementary domains: evidence-centric inference, which quantifies compatibility between data and models, and decision-centric inference, which guides actions under uncertainty. By embedding statistical tools within transparent and reproducible research workflows, the modern inferential paradigm moves beyond single-metric evaluation toward a multidimensional assessment of evidence and practical relevance.
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. The manuscript reviews the evolution of statistical inference from 2016 to 2026, critiquing traditional NHST for promoting dichotomous thinking and irreproducibility while synthesizing methodological advances (compatibility-based p-values, S-values, SESOI equivalence testing, Bayesian workflows, e-values, sequential inference) and systemic reforms (preregistration, Registered Reports, multiverse analysis, PRISMA 2020, CONSORT 2025). Its central claim is a conceptual unification of inference into two complementary domains: evidence-centric inference (quantifying data-model compatibility) and decision-centric inference (guiding actions under uncertainty), embedded in transparent open-science workflows for multidimensional evidence assessment.
Significance. If the unification is substantiated, the review could serve as a useful organizing framework for researchers navigating the post-NHST landscape, clarifying how existing tools and reforms fit together to support reproducible science. As a synthesis rather than a source of new theorems, empirical tests, or parameter-free derivations, its primary value lies in conceptual clarity and literature integration rather than novel methodological contributions.
major comments (2)
- [Abstract] Abstract and central claim: the proposed unification into evidence-centric and decision-centric domains is asserted as a key contribution but lacks explicit mapping of the listed tools (e.g., S-values vs. e-values) to each domain or a worked example showing how a single analysis would be partitioned; this makes it difficult to evaluate whether the distinction is additive or merely descriptive.
- The manuscript positions the 2016-2026 developments as forming a coherent 'ecosystem' without addressing documented tensions in the literature (e.g., between compatibility interpretations of p-values and decision-theoretic uses of e-values); a section contrasting these approaches with counter-examples would be needed to support the claim of clean complementarity.
minor comments (2)
- [Abstract] The abstract lists multiple specific tools and reforms but does not indicate the manuscript's structure (e.g., dedicated sections for each domain or a summary table); adding such an outline would improve readability.
- Terminology such as 'compatibility-based interpretation of p-values' and 'S-values' is introduced without immediate cross-references to primary sources or brief definitions, which may hinder readers unfamiliar with the 2016-2026 literature.
Simulated Author's Rebuttal
We thank the referee for their constructive comments, which help clarify how our proposed unification can be more explicitly substantiated. We address each major point below and have revised the manuscript to incorporate explicit mappings, a worked example, and discussion of tensions, thereby strengthening the central claim without altering the review's synthetic nature.
read point-by-point responses
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Referee: [Abstract] Abstract and central claim: the proposed unification into evidence-centric and decision-centric domains is asserted as a key contribution but lacks explicit mapping of the listed tools (e.g., S-values vs. e-values) to each domain or a worked example showing how a single analysis would be partitioned; this makes it difficult to evaluate whether the distinction is additive or merely descriptive.
Authors: We agree that an explicit mapping and worked example would improve evaluability of the framework. In the revised manuscript, we have added a new subsection (Section 4.3) that provides a table mapping each tool—compatibility-based p-values and S-values to evidence-centric inference (quantifying data-model compatibility), SESOI equivalence testing and Bayesian workflows to both domains depending on use, and e-values to decision-centric inference (guiding actions under uncertainty)—along with a concrete worked example using a single dataset to show partitioning within a preregistered open-science workflow. This demonstrates the distinction as additive, organizing tools into complementary roles for multidimensional assessment rather than purely descriptive. revision: yes
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Referee: The manuscript positions the 2016-2026 developments as forming a coherent 'ecosystem' without addressing documented tensions in the literature (e.g., between compatibility interpretations of p-values and decision-theoretic uses of e-values); a section contrasting these approaches with counter-examples would be needed to support the claim of clean complementarity.
Authors: We acknowledge that the original text did not explicitly contrast tensions, which could leave the complementarity claim open to question. In the revision, we have added a dedicated subsection (Section 5.2) that contrasts compatibility interpretations (e.g., p-values and S-values as measures of data-model fit) with decision-theoretic uses (e.g., e-values for sequential decision-making), including counter-examples where strict compatibility focus might undervalue action-guiding thresholds and vice versa. This supports the ecosystem claim by showing how the two domains accommodate such tensions through transparent workflows, without claiming perfect harmony in all applications. revision: yes
Circularity Check
No significant circularity in conceptual synthesis
full rationale
The paper is a literature review synthesizing methodological advances and open-science reforms from 2016-2026. It contains no equations, derivations, fitted parameters, or quantitative predictions. The claimed unification of statistical inference into evidence-centric and decision-centric domains is presented as a conceptual framing of existing tools (compatibility p-values, S-values, SESOI, Bayesian workflows, e-values, preregistration, Registered Reports) rather than a formal derivation that reduces to its own inputs. No self-citation chains, ansatzes, or renamings of known results function as load-bearing steps; the argument is self-contained against external benchmarks in the cited literature.
discussion (0)
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