Test and effect size details

In statsExpressions: Tidy Dataframes and Expressions with Statistical Details

#| label = "setup",
#| message = FALSE,
#| warning = FALSE,
#| include = FALSE,
#| echo = FALSE
source("setup.R")

This vignette can be cited as:

citation("statsExpressions")

Introduction

Here a go-to summary about statistical test carried out and the returned effect size for each function is provided. This should be useful if one needs to find out more information about how an argument is resolved in the underlying package or if one wishes to browse the source code. So, for example, if you want to know more about how one-way (between-subjects) ANOVA, you can run ?stats::oneway.test in your R console.

Abbreviations used: CI = Confidence Interval

Data requirements

All functions expect data in long (tidy) format — one row per observation. A few additional requirements are worth noting:

• Within-subjects (repeated measures) designs: The data must contain exactly one observation per subject per condition (a complete, balanced block design). If you have multiple trials per subject-condition cell, aggregate them first (e.g., by taking the mean) before passing the data. You can verify this with table(data$subject, data$condition) — every cell should equal 1.

• subject.id argument: For within-subjects designs, always specify subject.id explicitly. If omitted, the function pairs observations by row order within each condition, so any data that is not already sorted identically within every condition level can produce silently incorrect paired tests — even with exactly two conditions and no missing values.

• Missing data: Missing values are handled internally by removing any subject who has NA in any condition, ensuring a balanced design is maintained.

Summary of functionality

Summary of tests and effect sizes

Here a go-to summary about statistical test carried out and the returned effect size for each function is provided. This should be useful if one needs to find out more information about how an argument is resolved in the underlying package or if one wishes to browse the source code. So, for example, if you want to know more about how one-way (between-subjects) ANOVA, you can run ?stats::oneway.test in your R console.

centrality_description()

oneway_anova()

two_sample_test()

one_sample_test()

corr_test()

contingency_table()

meta_analysis()

Effect size interpretation

See {effectsize}'s interpretation functions to check different rules/conventions to interpret effect sizes:

https://easystats.github.io/effectsize/reference/index.html#section-interpretation

References

• For parametric and non-parametric effect sizes: https://easystats.github.io/effectsize/articles/

• For robust effect sizes: https://CRAN.R-project.org/package=WRS2/vignettes/WRS2.pdf

• For Bayesian posterior estimates: https://easystats.github.io/bayestestR/articles/bayes_factors.html

Suggestions

If you find any bugs or have any suggestions/remarks, please file an issue on GitHub: https://github.com/IndrajeetPatil/statsExpressions/issues

statsExpressions documentation built on Aug. 24, 2026, 5:13 p.m.