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Statistical Inference in the 21st Century: A World Beyond p < 0.05
- Moving to a World Beyond “p < 0.05”
- What Have We (Not) Learnt from Millions of Scientific Papers with P Values?
- Why is Getting Rid of P-Values So Hard? Musings on Science and Statistics
- Will the ASA's Efforts to Improve Statistical Practice be Successful? Some Evidence to the Contrary
- Correcting Corrupt Research: Recommendations for the Profession to Stop Misuse of p-Values
- Quality Control for Scientific Research: Addressing Reproducibility, Responsiveness, and Relevance
- The Role of Expert Judgment in Statistical Inference and Evidence-Based Decision-Making
- Expert Knowledge Elicitation: Subjective but Scientific
- Before p < 0.05 to Beyond p < 0.05: Using History to Contextualize p-Values and Significance Testing
- The Limited Role of Formal Statistical Inference in Scientific Inference
- Large-Scale Replication Projects in Contemporary Psychological Research
- Valid P-Values Behave Exactly as They Should: Some Misleading Criticisms of P-Values and Their Resolution With S-Values
- The p-Value Requires Context, Not a Threshold
- Assessing Statistical Results: Magnitude, Precision, and Model Uncertainty
- Putting the P-Value in its Place
- Evidence From Marginally Significant t Statistics
- The p-value Function and Statistical Inference
- p-Values, Bayes Factors, and Sufficiency
- Limitations of P-Values and R-squared for Stepwise Regression Building: A Fairness Demonstration in Health Policy Risk Adjustment
- An Introduction to Second-Generation p-Values
- A Proposed Hybrid Effect Size Plus p-Value Criterion: Empirical Evidence Supporting its Use
- Three Recommendations for Improving the Use of p-Values
- The False Positive Risk: A Proposal Concerning What to Do About p-Values
- Moving Towards the Post p < 0.05 Era via the Analysis of Credibility
- Blending Bayesian and Classical Tools to Define Optimal Sample-Size-Dependent Significance Levels
- How Effect Size (Practical Significance) Misleads Clinical Practice: The Case for Switching to Practical Benefit to Assess Applied Research Findings
- Abandon Statistical Significance
- Statistical Inference Enables Bad Science; Statistical Thinking Enables Good Science
- Inferential Statistics as Descriptive Statistics: There Is No Replication Crisis if We Don’t Expect Replication
- The New Statistics for Better Science: Ask How Much, How Uncertain, and What Else Is Known
- How Large Are Your G-Values? Try Gosset’s Guinnessometrics When a Little “p” Is Not Enough
- Predictive Inference and Scientific Reproducibility
- Treatment Choice With Trial Data: Statistical Decision Theory Should Supplant Hypothesis Testing
- Multiple Perspectives on Inference for Two Simple Statistical Scenarios
- Coup de Grâce for a Tough Old Bull: “Statistically Significant” Expires
- The World of Research Has Gone Berserk: Modeling the Consequences of Requiring “Greater Statistical Stringency” for Scientific Publication
- Assessing the Statistical Analyses Used in Basic and Applied Social Psychology After Their p-Value Ban
