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Parametric, non-parametric, robust, and Bayesian one-sample tests.


  type = "parametric",
  test.value = 0,
  alternative = "two.sided",
  digits = 2L,
  conf.level = 0.95,
  tr = 0.2,
  bf.prior = 0.707,
  effsize.type = "g",



A data frame (or a tibble) from which variables specified are to be taken. Other data types (e.g., matrix,table, array, etc.) will not be accepted. Additionally, grouped data frames from {dplyr} should be ungrouped before they are entered as data.


A numeric variable from the data frame data.


A character specifying the type of statistical approach:

  • "parametric"

  • "nonparametric"

  • "robust"

  • "bayes"

You can specify just the initial letter.


A number indicating the true value of the mean (Default: 0).


a character string specifying the alternative hypothesis, must be one of "two.sided" (default), "greater" or "less". You can specify just the initial letter.


Number of digits for rounding or significant figures. May also be "signif" to return significant figures or "scientific" to return scientific notation. Control the number of digits by adding the value as suffix, e.g. digits = "scientific4" to have scientific notation with 4 decimal places, or digits = "signif5" for 5 significant figures (see also signif()).


Scalar between 0 and 1 (default: 95% confidence/credible intervals, 0.95). If NULL, no confidence intervals will be computed.


Trim level for the mean when carrying out robust tests. In case of an error, try reducing the value of tr, which is by default set to 0.2. Lowering the value might help.


A number between 0.5 and 2 (default 0.707), the prior width to use in calculating Bayes factors and posterior estimates. In addition to numeric arguments, several named values are also recognized: "medium", "wide", and "ultrawide", corresponding to r scale values of 1/2, sqrt(2)/2, and 1, respectively. In case of an ANOVA, this value corresponds to scale for fixed effects.


Type of effect size needed for parametric tests. The argument can be "d" (for Cohen's d) or "g" (for Hedge's g).


Currently ignored.


The returned tibble data frame can contain some or all of the following columns (the exact columns will depend on the statistical test):

  • statistic: the numeric value of a statistic

  • df: the numeric value of a parameter being modeled (often degrees of freedom for the test)

  • df.error and df: relevant only if the statistic in question has two degrees of freedom (e.g. anova)

  • p.value: the two-sided p-value associated with the observed statistic

  • method: the name of the inferential statistical test

  • estimate: estimated value of the effect size

  • conf.low: lower bound for the effect size estimate

  • conf.high: upper bound for the effect size estimate

  • conf.level: width of the confidence interval

  • conf.method: method used to compute confidence interval

  • conf.distribution: statistical distribution for the effect

  • effectsize: the name of the effect size

  • n.obs: number of observations

  • expression: pre-formatted expression containing statistical details

For examples, see data frame output vignette.

One-sample tests

The table below provides summary about:

  • statistical test carried out for inferential statistics

  • type of effect size estimate and a measure of uncertainty for this estimate

  • functions used internally to compute these details

Hypothesis testing

TypeTestFunction used
ParametricOne-sample Student's t-teststats::t.test()
Non-parametricOne-sample Wilcoxon teststats::wilcox.test()
RobustBootstrap-t method for one-sample testWRS2::trimcibt()
BayesianOne-sample Student's t-testBayesFactor::ttestBF()

Effect size estimation

TypeEffect sizeCI available?Function used
ParametricCohen's d, Hedge's gYeseffectsize::cohens_d(), effectsize::hedges_g()
Non-parametricr (rank-biserial correlation)Yeseffectsize::rank_biserial()
Robusttrimmed meanYesWRS2::trimcibt()
Bayes FactordifferenceYesbayestestR::describe_posterior()


# for reproducibility

# ----------------------- parametric -----------------------

one_sample_test(mtcars, wt, test.value = 3)
#> # A tibble: 1 × 15
#>      mu statistic df.error p.value method            alternative effectsize
#>   <dbl>     <dbl>    <dbl>   <dbl> <chr>             <chr>       <chr>     
#> 1     3      1.26       31   0.218 One Sample t-test two.sided   Hedges' g 
#>   estimate conf.level conf.low conf.high conf.method conf.distribution n.obs
#>      <dbl>      <dbl>    <dbl>     <dbl> <chr>       <chr>             <int>
#> 1    0.217       0.95   -0.127     0.557 ncp         t                    32
#>   expression
#>   <list>    
#> 1 <language>

# ----------------------- non-parametric -------------------

one_sample_test(mtcars, wt, test.value = 3, type = "nonparametric")
#> # A tibble: 1 × 12
#>   statistic p.value method                    alternative effectsize       
#>       <dbl>   <dbl> <chr>                     <chr>       <chr>            
#> 1       319   0.308 Wilcoxon signed rank test two.sided   r (rank biserial)
#>   estimate conf.level conf.low conf.high conf.method n.obs expression
#>      <dbl>      <dbl>    <dbl>     <dbl> <chr>       <int> <list>    
#> 1    0.208       0.95   -0.184     0.543 normal         32 <language>

# ----------------------- robust ---------------------------

one_sample_test(mtcars, wt, test.value = 3, type = "robust")
#> # A tibble: 1 × 10
#>   statistic p.value n.obs method                                 effectsize  
#>       <dbl>   <dbl> <int> <chr>                                  <chr>       
#> 1      1.18   0.275    32 Bootstrap-t method for one-sample test Trimmed mean
#>   estimate conf.level conf.low conf.high expression
#>      <dbl>      <dbl>    <dbl>     <dbl> <list>    
#> 1     3.20       0.95     2.85      3.54 <language>

# ----------------------- Bayesian -------------------------

one_sample_test(mtcars, wt, test.value = 3, type = "bayes")
#> # A tibble: 1 × 16
#>   term       effectsize      estimate conf.level conf.low conf.high    pd
#>   <chr>      <chr>              <dbl>      <dbl>    <dbl>     <dbl> <dbl>
#> 1 Difference Bayesian t-test    0.195       0.95   -0.165     0.555  0.86
#>   prior.distribution prior.location prior.scale  bf10 method         
#>   <chr>                       <dbl>       <dbl> <dbl> <chr>          
#> 1 cauchy                          0       0.707 0.387 Bayesian t-test
#>   conf.method log_e_bf10 n.obs expression
#>   <chr>            <dbl> <int> <list>    
#> 1 ETI             -0.950    32 <language>