| Abandon Statistical Significance |
84 |
| R-squared for Bayesian Regression Models |
53 |
| Forecasting at Scale |
49 |
| Inferential Statistics as Descriptive Statistics: There Is No Replication Crisis if We Don't Expect Replication |
37 |
| Three Recommendations for Improving the Use of p-Values |
29 |
| Optimal Whitening and Decorrelation |
27 |
| Coup de Grace for a Tough Old Bull: Statistically Significant Expires |
27 |
| Valid P-Values Behave Exactly as They Should: Some Misleading Criticisms of P-Values and Their Resolution With S-Values |
24 |
| The False Positive Risk: A Proposal Concerning What to Do About p-Values |
20 |
| Expert Knowledge Elicitation: Subjective but Scientific |
19 |
| The p-Value Requires Context, Not a Threshold |
15 |
| The New Statistics for Better Science: Ask How Much, How Uncertain, and What Else Is Known |
15 |
| The Perils of Balance Testing in Experimental Design: Messy Analyses of Clean Data |
15 |
| Large-Scale Replication Projects in Contemporary Psychological Research |
13 |
| What Have We (Not) Learnt from Millions of Scientific Papers with P Values? |
13 |
| Why is Getting Rid of P-Values So Hard? Musings on Science and Statistics |
12 |
| Assessing the Statistical Analyses Used in Basic and Applied Social Psychology After Their p-Value Ban |
11 |
| Packaging Data Analytical Work Reproducibly Using R (and Friends) |
11 |
| Data Organization in Spreadsheets |
10 |
| Moving Towards the Post p < 0.05 Era via the Analysis of Credibility |
10 |
| Statistical Inference Enables Bad Science; Statistical Thinking Enables Good Science |
9 |
| The Role of Expert Judgment in Statistical Inference and Evidence-Based Decision-Making |
9 |
| Putting the P-Value in its Place |
8 |
| Bayesian Inference for Kendall's Rank Correlation Coefficient |
8 |
| The Tale of Cochran's Rule: My Contingency Table has so Many Expected Values Smaller than What Am I to Do? |
8 |
| Sharpening Jensen's Inequality |
7 |
| An Introduction to Second-Generation p-Values |
7 |
| Assessing Statistical Results: Magnitude, Precision, and Model Uncertainty |
7 |
| The Limited Role of Formal Statistical Inference in Scientific Inference |
7 |
| Infrastructure and Tools for Teaching Computing Throughout the Statistical Curriculum |
7 |
| Teaching Bayes' Theorem: Strength of Evidence as Predictive Accuracy |
6 |
| Why are p-Values Controversial? |
6 |
| Before p < 0.05 to Beyond p < 0.05: Using History to Contextualize p-Values and Significance Testing |
6 |
| A Guide to Teaching Data Science |
6 |
| The p-value Function and Statistical Inference |
5 |
| How Effect Size (Practical Significance) Misleads Clinical Practice: The Case for Switching to Practical Benefit to Assess Applied Research Findings |
5 |
| Predictive Inference and Scientific Reproducibility |
5 |
| How Large Are Your G-Values? Try Gosset's Guinnessometrics When a Little pIs Not Enough |
5 |
| Multiple Perspectives on Inference for Two Simple Statistical Scenarios |
5 |
| Inference and Decision Making for 21st-Century Drug Development and Approval |
5 |
| Five Nonobvious Changes in Editorial Practice for Editors and Reviewers to Consider When Evaluating Submissions in A Post p < 0.05 Universe |
5 |
| Beyond Calculations: A Course in Statistical Thinking |
5 |
| An Improved Boxplot for Univariate Data |
5 |
| The Wilcoxon-Mann-Whitney Procedure Fails as a Test of Medians |
5 |
| Excuse Me, Do You Have a Moment to Talk About Version Control? |
4 |
| Extending R with C plus plus : A Brief Introduction to Rcpp |
4 |
| Minimum Volume Confidence Sets for Two-Parameter Exponential Distributions |
4 |
| Structural Equation Models for Dealing With Spatial Confounding |
4 |
| Treatment Choice With Trial Data: Statistical Decision Theory Should Supplant Hypothesis Testing |
4 |
| A Note on Collinearity Diagnostics and Centering |
4 |
