independentSamplesTTest: Independent samples t-test

Description

Runs an independent-samples t-test and prints the results in a readable format.

Usage

independentSamplesTTest(
  formula,
  data = NULL,
  var.equal = FALSE,
  one.sided = FALSE,
  conf.level = 0.95
)

Value

Prints a summary showing the outcome and grouping variable names, group means and standard deviations, null and alternative hypotheses, test results (t-statistic, degrees of freedom, p-value), a confidence interval, and Cohen's d as a measure of effect size. The underlying results are also returned as a list, so the output can be assigned to a variable and inspected if needed.

Arguments

formula: A formula of the form outcome ~ group, where outcome is the numeric variable being measured and group is a factor with exactly two levels.
data: An optional data frame containing the variables named in formula. Tibbles are accepted and converted automatically. If data is omitted the variables are looked up in the workspace.
var.equal: Set to TRUE to run Student's t-test, which assumes equal group variances. The default (FALSE) runs Welch's t-test, which is safer when variances may differ between groups.
one.sided: Set to FALSE (default) for a two-sided test. Set to the name of the group expected to have the larger mean for a one-sided test (e.g., one.sided = "group2").
conf.level: The confidence level for the confidence interval. The default is 0.95 for a 95% interval.

Details

Runs an independent-samples t-test comparing the means of two groups, and prints the results in a beginner-friendly format. The calculations are done by t.test and cohensD. When var.equal = TRUE, Cohen's d uses a pooled standard deviation; when var.equal = FALSE (Welch's test), it uses the "unequal" method. Cases with missing values are removed with a warning.

Examples

Run this code

df <- data.frame(
  rt = c(451, 562, 704, 324, 505, 600, 829),
  cond = factor(x = c(1, 1, 1, 2, 2, 2, 2), labels = c("group1", "group2"))
)

# Welch's t-test (the default, does not assume equal variances)
independentSamplesTTest(rt ~ cond, df)

# Student's t-test (assumes equal variances)
independentSamplesTTest(rt ~ cond, df, var.equal = TRUE)

# one-sided test: is group1 larger?
independentSamplesTTest(rt ~ cond, df, one.sided = "group1")

# missing values are removed with a warning
df$rt[1] <- NA
df$cond[7] <- NA
independentSamplesTTest(rt ~ cond, df)