The t-test can be defined as a statistical test used to compare two means. This guide provide multiple tutorials describing the different types of t-test, including:
- one-sample t-tests. Compares one-sample mean to a know standard mean.
- independent samples t-tests: Student’s t-test and Welch’s t-test. Compares two independent groups
- paired samples t-test. Compares two related samples.
You will learn the t-test formula and how to:
- Compute the different t-tests in R
- Check t-test assumptions
- Calculate and report t-test effect size using Cohen’s d.
Related BookPractical Statistics in R II - Comparing Groups: Numerical Variables
Make sure you have installed the following R packages:
tidyversefor data manipulation and visualization
ggpubrfor creating easily publication ready plots
rstatixprovides pipe-friendly R functions for easy statistical analyses.
datarium: contains required data sets for this chapter.
Start by loading the following required packages:
library(tidyverse) library(ggpubr) library(rstatix)
Examples of R codes
Comparing two independent groups:
# Data preparation data("genderweight", package = "datarium") head(genderweight, 3)
## # A tibble: 3 x 3 ## id group weight ## <fct> <fct> <dbl> ## 1 1 F 61.6 ## 2 2 F 64.6 ## 3 3 F 66.2
# Statistical test stat.test <- genderweight %>% t_test(weight ~ group) %>% add_significance() stat.test
## # A tibble: 1 x 9 ## .y. group1 group2 n1 n2 statistic df p p.signif ## <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <chr> ## 1 weight F M 20 20 -20.8 26.9 4.30e-18 ****
# Visualization: # Create a box-plot bxp <- ggboxplot( genderweight, x = "group", y = "weight", ylab = "Weight", xlab = "Groups", add = "jitter" ) # Add p-value and significance levels stat.test <- stat.test %>% add_xy_position(x = "group") bxp + stat_pvalue_manual(stat.test, tip.length = 0) + labs(subtitle = get_test_label(stat.test, detailed = TRUE))
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