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

library(readxl)
library(ggpubr)
library(dplyr)
library(effectsize)
library(effsize)
A6Q3 <- read_excel("/Users/murphy/Downloads/A6Q3.xlsx")
A6Q3 %>%
  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 cardio    74.7   73.3  7.57    25
## 2 nocardio  70.8   69.5  7.35    25
hist(A6Q3$Weight[A6Q3$Exercise == "nocardio"],
     main = "Histogram of No Cardio Weight",
     xlab = "Value",
     ylab = "Frequency",
     col = "lightblue",
     border = "black",
     breaks = 10)

hist(A6Q3$Weight[A6Q3$Exercise == "cardio"],
     main = "Histogram of Cardio Weight",
     xlab = "Value",
     ylab = "Frequency",
     col = "lightgreen",
     border = "black",
     breaks = 10)

Group 1: No Cardio The first variable looks abnormally distributed. The data is symmetrical. The data does not have a proper bell curve.

Group 2: Cardio The second variable looks abnormally distributed. The data is negatively skewed. The data does not have a proper bell curve.

ggboxplot(A6Q3, x = "Exercise", y = "Weight",
          color = "Exercise",
          palette = "jco",
          add = "jitter")

Boxplot 1: No Cardio There are dots outside the boxplot. The dots are past the whiskers. The dots are not very far away from the whiskers. Based on these findings, the boxplot is normal.

Boxplot 2: Cardio There are dots outside the boxplot. The dots are past the whiskers. The dots are not very far away from the whiskers. Based on these findings, the boxplot is normal.

shapiro.test(A6Q3$Weight[A6Q3$Exercise == "nocardio"])
## 
##  Shapiro-Wilk normality test
## 
## data:  A6Q3$Weight[A6Q3$Exercise == "nocardio"]
## W = 0.97686, p-value = 0.8166
shapiro.test(A6Q3$Weight[A6Q3$Exercise == "cardio"])
## 
##  Shapiro-Wilk normality test
## 
## data:  A6Q3$Weight[A6Q3$Exercise == "cardio"]
## W = 0.96745, p-value = 0.5812

Group 1: No Cardio The first group is normally distributed, (p = 0.817).

Group 2: Cardio The second group is normally distributed, (p = 0.581).

t.test(Weight ~ Exercise, data = A6Q3, var.equal = TRUE)
## 
##  Two Sample t-test
## 
## data:  Weight by Exercise
## t = 1.8552, df = 48, p-value = 0.06971
## alternative hypothesis: true difference in means between group cardio and group nocardio is not equal to 0
## 95 percent confidence interval:
##  -0.3280454  8.1605622
## sample estimates:
##   mean in group cardio mean in group nocardio 
##               74.73336               70.81710
cohens_d_result <- cohens_d(Weight ~ Exercise, data = A6Q3, pooled_sd = TRUE)
print(cohens_d_result)
## Cohen's d |        95% CI
## -------------------------
## 0.52      | [-0.04, 1.09]
## 
## - Estimated using pooled SD.

An Independent T-Test was conducted to determine if there was a difference in the weight of participants between participants who did not do cardio and participants who did do cardio. No Cardio participants scores (M = 70.82, SD = 7.35) were significantly different from Cardio participants scores (M = 74.73, SD = 7.57), t(48) = 1.86, p > .05. The effect size was medium, Cohen’s d = 0.52.