Independent samples t-test

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
)

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.

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.

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.

See also

t.test, oneSampleTTest, pairedSamplesTTest, cohensD

Examples

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)
#> 
#>    Welch's independent samples t-test 
#> 
#> Outcome variable:   rt 
#> Grouping variable:  cond 
#> 
#> Descriptive statistics: 
#>              group1  group2
#>    mean     572.333 564.500
#>    std dev. 126.816 210.239
#> 
#> Hypotheses: 
#>    null:        population means equal for both groups
#>    alternative: different population means in each group
#> 
#> Test results: 
#>    t-statistic:  0.061 
#>    degrees of freedom:  4.89 
#>    p-value:  0.954 
#> 
#> Other information: 
#>    two-sided 95% confidence interval:  [-323.703, 339.37] 
#>    estimated effect size (Cohen's d):  0.045 
#> 

# Student's t-test (assumes equal variances)
independentSamplesTTest(rt ~ cond, df, var.equal = TRUE)
#> 
#>    Student's independent samples t-test 
#> 
#> Outcome variable:   rt 
#> Grouping variable:  cond 
#> 
#> Descriptive statistics: 
#>              group1  group2
#>    mean     572.333 564.500
#>    std dev. 126.816 210.239
#> 
#> Hypotheses: 
#>    null:        population means equal for both groups
#>    alternative: different population means in each group
#> 
#> Test results: 
#>    t-statistic:  0.056 
#>    degrees of freedom:  5 
#>    p-value:  0.957 
#> 
#> Other information: 
#>    two-sided 95% confidence interval:  [-348.567, 364.234] 
#>    estimated effect size (Cohen's d):  0.043 
#> 

# one-sided test: is group1 larger?
independentSamplesTTest(rt ~ cond, df, one.sided = "group1")
#> 
#>    Welch's independent samples t-test 
#> 
#> Outcome variable:   rt 
#> Grouping variable:  cond 
#> 
#> Descriptive statistics: 
#>              group1  group2
#>    mean     572.333 564.500
#>    std dev. 126.816 210.239
#> 
#> Hypotheses: 
#>    null:        population means are equal, or smaller for group 'group1' 
#>    alternative: population mean is larger for group 'group1' 
#> 
#> Test results: 
#>    t-statistic:  0.061 
#>    degrees of freedom:  4.89 
#>    p-value:  0.477 
#> 
#> Other information: 
#>    one-sided 95% confidence interval:  [-251.588, Inf] 
#>    estimated effect size (Cohen's d):  0.045 
#> 

# missing values are removed with a warning
df$rt[1] <- NA
df$cond[7] <- NA
independentSamplesTTest(rt ~ cond, df)
#> Warning: 2 case(s) removed due to missingness
#> 
#>    Welch's independent samples t-test 
#> 
#> Outcome variable:   rt 
#> Grouping variable:  cond 
#> 
#> Descriptive statistics: 
#>              group1  group2
#>    mean     633.000 476.333
#>    std dev. 100.409 140.215
#> 
#> Hypotheses: 
#>    null:        population means equal for both groups
#>    alternative: different population means in each group
#> 
#> Test results: 
#>    t-statistic:  1.455 
#>    degrees of freedom:  2.867 
#>    p-value:  0.246 
#> 
#> Other information: 
#>    two-sided 95% confidence interval:  [-195.166, 508.499] 
#>    estimated effect size (Cohen's d):  1.285 
#>