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The Publishing Handbook

How to design, report and defend a statistical analysis in a paper.

47 of 62 chapters published, across 10 parts. Every chapter runs R in your browser.

Start reading → All books

Part 1

Before the data

1Study Design Types: RCT, Cohort, Case-Control→ 2Power Analysis by Simulation in R→ 3Pre-Analysis Plans in R: Commit Before You Analyze→ 4Pre-Registration for R Analysis: OSF, AsPredicted & Analysis Plans→

Part 2

Data you can defend

5Missing Data Types in R: MCAR, MAR, MNAR→ 6How to Report Multiple Imputation in a Paper→ 7How to Document Your Data Cleaning Steps→

Part 3

Choosing and defending the analysis

8Justifying Your Choice of Statistical Test→ 9Sensitivity Analysis in R: How Robust Are Your Statistical Conclusions?→ 10Influential Observations in R: Cook's Distance→

Part 4

Reporting the numbers

11Report Statistics in R: The Numbers to Include and the Words to Use→ 12Which Effect Size to Report, and When→ 13How to Report Confidence Intervals in a Paper→

14How to Report p-Values in a PaperSoon

Part 5

Tables and figures for journals

15Table 1 in R with gtsummary and tableoneSoon

16Regression Tables in R: modelsummary vs stargazer→

17Journal Figure Requirements: Size, DPI, FontsSoon

18Accessible Data Visualization in R: Color Vision and Contrast→ 19Error Bars in ggplot2: SD, SE or CI, Done Right→

Part 6

Writing it up

20How to Write a Statistics Methods SectionSoon

21How to Write a Results Section with StatisticsSoon

22How to Write Study LimitationsSoon

23Statistical Terms to Avoid in a PaperSoon

Part 7

Reporting guidelines

24Reporting Guidelines: Which One Applies to YouSoon

25CONSORT Flow Diagram in RSoon

26STROBE Reporting in R for Observational StudiesSoon

27PRISMA and TRIPOD Reporting in RSoon

Part 8

Reading a review

28How to Answer Statistical Reviewer Comments in R→

Part 9

The thirty objections

29Non-Normal Residuals in Peer Review→ 30Unequal Variance in Peer Review→ 31Non-Independent Observations in Peer Review→ 32Autocorrelated Residuals in Peer Review→ 33Multicollinearity in Peer Review→ 34Nonlinear Relationships in Peer Review→ 35Proportional Hazards in Peer Review→ 36Missing Power Analysis in Peer Review→ 37Unadjusted Confounding in Peer Review→ 38Baseline Imbalance in Peer Review→ 39Selection Bias in Peer Review→ 40Non-Comparable Control Groups in Peer Review→ 41Multiple Comparisons in Peer Review→ 42Unplanned Subgroup Analyses in Peer Review→ 43Borderline p-Values in Peer Review→ 44Exploratory vs Confirmatory Analysis in Peer Review→ 45Dichotomising Continuous Variables in Peer Review→ 46Outlier Removal in Peer Review→ 47Missing Effect Sizes in Peer Review→ 48Missing Confidence Intervals in Peer Review→ 49Trending Toward Significance in Peer Review→ 50Model Fit Statistics in Peer Review→ 51Missing Data Reporting in Peer Review→ 52Ordinal Data in Peer Review→ 53Mixed Models in Peer Review→ 54Link Function Choice in Peer Review→ 55Zero-Inflation in Peer Review→ 56Too Many Predictors in Peer Review→ 57Interaction Terms in Peer Review→ 58Code and Data Sharing in Peer Review→

Part 10

Response, revision, and after

59How to Write a Response to Reviewers LetterSoon

60How to Disagree With a ReviewerSoon

61Second Round Review: What to DoSoon

62Corrections and Retractions: What to DoSoon