Is there a difference in body weight (kg) between participants who lift weights versus participants who do not lift weights?

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)

A6Q4 <- read_excel("~/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()
  )
## # A tibble: 2 × 5
##   Exercise  Mean Median    SD     N
##   <chr>    <dbl>  <dbl> <dbl> <int>
## 1 lift     120.   116.   53.3    25
## 2 nolift    33.0   40.8  56.7    25
hist(A6Q4$Weight[A6Q4$Exercise == "lift"],
     main = "Histogram of lift Weight",
     xlab = "Value",
     ylab = "Frequency",
     col = "lightblue",
     border = "black",
     breaks = 10)

hist(A6Q4$Weight[A6Q4$Exercise == "nolift"],
     main = "Histogram of nolift Weight",
     xlab = "Value",
     ylab = "Frequency",
     col = "lightgreen",
     border = "black",
     breaks = 10)

Group 1: Lift The first variable looks abnormally distributed. The data is positively skewed. The data does not have a proper bell curve.

Group 2: No Lift The second variable looks abnormally distributed. The data is negatively skewed. The data does not have a proper bell curve.

ggboxplot(A6Q4, x = "Exercise", y = "Weight",
          color = "Exercise",
          palette = "jco",
          add = "jitter")

Boxplot 1: Lift There are dots outside the boxplot. The dots are not close to the whiskers. The dots are very far away from the whiskers. The outliers are not balanced. Based on these findings, the boxplot is not normal.

Boxplot 2: No Lift There is one dots outside the boxplot. The dots are not close to the whiskers. The dots are very far away from the whiskers. The outliers are not balanced. Based on these findings, the boxplot is not normal.

shapiro.test(A6Q4$Weight[A6Q4$Exercise == "lift"])
## 
##  Shapiro-Wilk normality test
## 
## data:  A6Q4$Weight[A6Q4$Exercise == "lift"]
## W = 0.78786, p-value = 0.0001436
shapiro.test(A6Q4$Weight[A6Q4$Exercise == "nolift"])
## 
##  Shapiro-Wilk normality test
## 
## data:  A6Q4$Weight[A6Q4$Exercise == "nolift"]
## W = 0.70002, p-value = 7.294e-06

Group 1: Lift The first group is abnormally distributed, (p < 0.001).

Group 2: No Lift The second group is abnormally distributed, (p < 0.001).

wilcox.test(Weight ~ Exercise, data = A6Q4)
## 
##  Wilcoxon rank sum exact test
## 
## data:  Weight by Exercise
## W = 603, p-value = 7.132e-11
## alternative hypothesis: true location shift is not equal to 0
mw_effect <- cliff.delta(Weight ~ Exercise, data = A6Q4)
print(mw_effect)
## 
## Cliff's Delta
## 
## delta estimate: 0.9296 (large)
## 95 percent confidence interval:
##     lower     upper 
## 0.7993841 0.9764036

A Mann-Whitney U test was conducted to determine if there was a difference in Weight between Lifting and Not Lifting Lift scores (Mdn = 116) were significantly different from No Lift scores (Mdn = 40.8) U = 603, p < 0.001 The effect size was large, Cliff’s Delta = .93.