```{r} library(dplyr) library(effectsize) library(effsize) library(readxl) library(ggpubr) A6Q4 <- read_excel(“C:/Users/User/Downloads/A6Q4.xlsx”) A6Q4 %>% group_by(Exercise) %>% summarise( Mean = mean(Weight, na.rm = TRUE), Median = median(Weight, na.rm = TRUE), SD = sd(Weight, na.rm = TRUE), N = n() ) hist(A6Q4\(Weight[A6Q4\)Exercise == “nolift”], breaks = 15, col = “skyblue”, border = “white”) hist(A6Q4\(Weight[A6Q4\)Exercise == “lift”], breaks = 15, col = “firebrick”, border = “white”)

#Data for nolift appears abnormally distributed. #Data for lift appears abnormally distributed. ggboxplot(A6Q4, x = “Exercise”, y = “Weight”, color = “Exercise”, palette = “jco”, add = “jitter”) # The nolift boxplot has outliers. # The lift boxplot has outliers. shapiro.test(A6Q4\(Weight[A6Q4\)Exercise == “nolift”]) shapiro.test(A6Q4\(Weight[A6Q4\)Exercise == “lift”]) #The nolift group is abnormally distributed, (p < .000). #The lift group is abnormally distributed, (p < .000). wilcox.test(Weight ~ Exercise, data = A6Q4) mw_effect <- cliff.delta(Weight ~ Exercise, data = A6Q4) print(mw_effect) #A Mann-Whitney U test was conducted to determine if there was a difference in Weight (kg) between participants who lift weights and those who do not lift weights. ##Here, I did not specify the unit (kg) and I did not describe the two groups, simply listing them as nolift and lift. #Those who do not lift weights (Mdn = 40.8) were significantly different from those who lift weights (Mdn = 116), W = 603, p < .001. ##Here, I again did not describe the two groups, simply listing them as nolift and lift. I also mistakenly write p<.000 rather than p<.001. #The effect size was large, Cliff’s Delta = .930. ```