T-Test Analysis

Table 1: Independent Samples T-Test Results

# FALLBACK LOGIC
if(!exists("raw_data")) {
  # Sample Data if run independently
  raw_data <- data.frame(
    gender = sample(c("Male", "Female"), 50, replace = TRUE),
    anxiety_score = rnorm(50, 20, 5)
  )
}

# 1. Run T-Test
# We use standard base R t.test for simplicity, formatted with a tidy approach if broom is available
# For this course consistency, we likely want straightforward output

t_result <- t.test(anxiety_score ~ gender, data = raw_data)

# 2. Print Result
print(t_result)


    Welch Two Sample t-test

data:  anxiety_score by gender
t = -0.98744, df = 24.148, p-value = 0.3332
alternative hypothesis: true difference in means between group Female and group Male is not equal to 0
95 percent confidence interval:
 -3.975957  1.402091
sample estimates:
mean in group Female   mean in group Male 
            19.61610             20.90303

# 3. APA Style Reporting (Optional automated text)
# "There was a significant difference between groups (t(df) = x.xx, p = .xxx)..."

T-Test Analysis

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