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)

Group 1: No Cardio The first variable looks normally distributed. The data is symmetrical. The data has a proper bell curve.

I thought the data did not have a proper bell curve. Changed bullet number three to state the data does have a proper bell curve.

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

Group 2: Cardio The second variable looks normally distributed. The data is symmetrical. The data has 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

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 not significantly different from Cardio participants scores (M = 74.73, SD = 7.57), t(48) = 1.86, p > .05.

The p value was greater than 0.05, so the results were not significantly different. Additionally, an effect size test should not have been run. I have removed the code for the cohens_d calculation and updated the report removing the sentence on the effect size.