Course description
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.
Contents:
Related Book
Practical Statistics in R II - Comparing Groups: Numerical VariablesPrerequisites
Make sure you have installed the following R packages:
tidyversefor data manipulation and visualizationggpubrfor creating easily publication ready plotsrstatixprovides 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))
Version:
Français
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