## Prerequisites

```
# Load required R packages
library(tidyverse)
library(rstatix)
library(ggpubr)
```

## Data preparation

We’ll use the self-esteem score dataset measured over three time points. The data is available in the datarium package.

```
# Wide format
data("selfesteem", package = "datarium")
head(selfesteem, 3)
```

```
## # A tibble: 3 x 4
## id t1 t2 t3
## <int> <dbl> <dbl> <dbl>
## 1 1 4.01 5.18 7.11
## 2 2 2.56 6.91 6.31
## 3 3 3.24 4.44 9.78
```

```
# Gather columns t1, t2 and t3 into long format
# Convert id and time into factor variables
selfesteem <- selfesteem %>%
gather(key = "time", value = "score", t1, t2, t3) %>%
convert_as_factor(id, time)
head(selfesteem, 3)
```

```
## # A tibble: 3 x 3
## id time score
## <fct> <fct> <dbl>
## 1 1 t1 4.01
## 2 2 t1 2.56
## 3 3 t1 3.24
```

## Run paired pairwise t-tests

You can perform multiple **pairwise paired t-tests** between the levels of the within-subjects factor (here `time`

). P-values are adjusted using the Bonferroni multiple testing correction method.

```
stat.test <- selfesteem %>%
pairwise_t_test(
score ~ time, paired = TRUE,
p.adjust.method = "bonferroni"
)
stat.test
```

```
## # A tibble: 3 x 10
## .y. group1 group2 n1 n2 statistic df p p.adj p.adj.signif
## * <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <dbl> <chr>
## 1 score t1 t2 10 10 -4.97 9 0.000772 0.002 **
## 2 score t1 t3 10 10 -13.2 9 0.000000334 0.000001 ****
## 3 score t2 t3 10 10 -4.87 9 0.000886 0.003 **
```

All the pairwise differences are statistically significant.

## Visualization: box plots with p-values

```
# Create the plot
myplot <- ggboxplot(selfesteem, x = "time", y = "score", add = "point")
# Add statistical test p-values
stat.test <- stat.test %>% add_xy_position(x = "time")
myplot + stat_pvalue_manual(stat.test, label = "p.adj.signif")
```

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Version: Français

Hi, thanks for the nice guide! But why are P-values adjusted using the Bonferroni multiple testing correction method?