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library(readxl)
library(ggpubr)
## Loading required package: ggplot2
library(dplyr)
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
library(effectsize)
library(effsize)
Dataset6.4 <- read_excel("C:/Users/tanie/Downloads/Dataset6.4.xlsx")
Before <- Dataset6.4$Stress_Pre
After <- Dataset6.4$Stress_Post

Differences <- After - Before
mean(Before, na.rm = TRUE)
## [1] 51.53601
median(Before, na.rm = TRUE)
## [1] 47.24008
sd(Before, na.rm = TRUE)
## [1] 17.21906
mean(After, na.rm = TRUE)
## [1] 41.4913
median(After, na.rm = TRUE)
## [1] 40.84836
sd(After, na.rm = TRUE)
## [1] 18.88901
hist(Differences,
     main = "Histogram of Difference Scores",
     xlab = "Value",
     ylab = "Frequency",
     col = "blue",
     border = "black",
     breaks = 20)

The histogram is negatively skewed and is not normal

boxplot(Differences,
        main = "Distribution of Score Differences (After - Before)",
        ylab = "Difference in Scores",
        col = "blue",
        border = "darkblue")

There are outliers far away from the whiskers . the data is not normal

shapiro.test(Differences)
## 
##  Shapiro-Wilk normality test
## 
## data:  Differences
## W = 0.87495, p-value = 0.0008963

p-value is less than.05, the data in not normal we should proceed with Wilcoxon sign rank

wilcox.test(Before, After, paired = TRUE)
## 
##  Wilcoxon signed rank exact test
## 
## data:  Before and After
## V = 620, p-value = 2.503e-09
## alternative hypothesis: true location shift is not equal to 0

There was no significant difference in stress levels between Stress_Pre (Mdn = 47.24) and Stress_Post (Mdn = 40.85), V = 620, p = 2.5.