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)
## 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)

A6Q3 <- read_excel("~/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 == "cardio"],
     main = "Histogram of Cardio Weights",
     xlab = "Value",
     ylab = "Frequency",
     col = "lightblue",
     border = "black",
     breaks = 10)

hist(A6Q3$Weight[A6Q3$Exercise == "nocardio"],
     main = "Histogram of No Cardio Weights",
     xlab = "Value",
     ylab = "Frequency",
     col = "lightgreen",
     border = "black",
     breaks = 10)

Group 1: Cardio: The first variable looks normally distributed. The data is negatively skewed. The data has a proper bell curve.

Group 2: No Cario: The second variable looks normally distributed. The data is positively skewed. The data has a proper bell curve.

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

Boxplot 1: Cardio: There are dots outside the boxplot. The dots are close to the whiskers. The dots are very far away from the whiskers. Based on these findings, the boxplot is normal.

Boxplot 2: No Cardio: There are dots outside the boxplot. The dots are close to the whiskers. Based on these findings, the boxplot is normal.

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

Group 1: Cardio: The first group is normally distributed, (p = 0.581).

Group 2: No Cardio: The second group is normally distributed, (p = 0.817).

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

An Independent T-Test was conducted to determine if there was a difference in Exercise between Cardio and No Cardio. Cardio scores (M = 74.70, SD = 7.57) were significantly different from No Cardio scores (M = 70.80, SD = 7.35), t(48) = 1.856, p = 0.070