One sample t-test

Runs a one-sample t-test and prints the results in a readable format.

Usage

oneSampleTTest(x, mu, one.sided = FALSE, conf.level = 0.95)

Arguments

x

A numeric vector containing the data to be tested.

mu

The hypothesised population mean to test against.

one.sided

Set to FALSE (default) for a two-sided test. Set to "greater" if you expect the population mean to be above mu, or "less" if you expect it to be below.

conf.level

The confidence level for the confidence interval. The default is 0.95 for a 95% interval.

Value

Prints a summary showing the variable name, descriptive statistics, 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 a one-sample t-test comparing the mean of x to the hypothesised value mu, and prints the results in a beginner-friendly format. The calculations are done by t.test and cohensD. Missing values in x are removed with a warning.

See also

t.test, pairedSamplesTTest, independentSamplesTTest, cohensD

Examples

likert <- c(3, 1, 4, 1, 4, 6, 7, 2, 6, 6, 7)

# two-sided test (the default)
oneSampleTTest(x = likert, mu = 4)
#> 
#>    One sample t-test 
#> 
#> Data variable:   likert 
#> 
#> Descriptive statistics: 
#>             likert
#>    mean      4.273
#>    std dev.  2.284
#> 
#> Hypotheses: 
#>    null:        population mean equals 4 
#>    alternative: population mean not equal to 4 
#> 
#> Test results: 
#>    t-statistic:  0.396 
#>    degrees of freedom:  10 
#>    p-value:  0.7 
#> 
#> Other information: 
#>    two-sided 95% confidence interval:  [2.738, 5.807] 
#>    estimated effect size (Cohen's d):  0.119 
#> 

# one-sided test: is the mean greater than 4?
oneSampleTTest(x = likert, mu = 4, one.sided = "greater")
#> 
#>    One sample t-test 
#> 
#> Data variable:   likert 
#> 
#> Descriptive statistics: 
#>             likert
#>    mean      4.273
#>    std dev.  2.284
#> 
#> Hypotheses: 
#>    null:        population mean less than or equal to 4 
#>    alternative: population mean greater than 4 
#> 
#> Test results: 
#>    t-statistic:  0.396 
#>    degrees of freedom:  10 
#>    p-value:  0.35 
#> 
#> Other information: 
#>    one-sided 95% confidence interval:  [3.024, Inf] 
#>    estimated effect size (Cohen's d):  0.119 
#> 

# wider confidence interval
oneSampleTTest(x = likert, mu = 4, conf.level = 0.99)
#> 
#>    One sample t-test 
#> 
#> Data variable:   likert 
#> 
#> Descriptive statistics: 
#>             likert
#>    mean      4.273
#>    std dev.  2.284
#> 
#> Hypotheses: 
#>    null:        population mean equals 4 
#>    alternative: population mean not equal to 4 
#> 
#> Test results: 
#>    t-statistic:  0.396 
#>    degrees of freedom:  10 
#>    p-value:  0.7 
#> 
#> Other information: 
#>    two-sided 99% confidence interval:  [2.09, 6.456] 
#>    estimated effect size (Cohen's d):  0.119 
#> 

# missing values are removed with a warning
likert <- c(3, NA, 4, NA, 4, 6, 7, NA, 6, 6, 7)
oneSampleTTest(x = likert, mu = 4)
#> Warning: 3 case(s) removed due to missingness
#> 
#>    One sample t-test 
#> 
#> Data variable:   likert 
#> 
#> Descriptive statistics: 
#>             likert
#>    mean      5.375
#>    std dev.  1.506
#> 
#> Hypotheses: 
#>    null:        population mean equals 4 
#>    alternative: population mean not equal to 4 
#> 
#> Test results: 
#>    t-statistic:  2.582 
#>    degrees of freedom:  7 
#>    p-value:  0.036 
#> 
#> Other information: 
#>    two-sided 95% confidence interval:  [4.116, 6.634] 
#>    estimated effect size (Cohen's d):  0.913 
#>