# Simple t-Test Use Case

#### Meike Snijder-Steinhilber

#### 2026-05-06

- The data set `df_stress` is included in the `sprtt` package. Thus, the data set is available when the package is loaded.

- In the R code sections:

  - `# comment`: is a comment
  - `function()`: is R code
  - `#> results of function()`: is console output

This vignette describes an exemplary use case to improve the understanding of the package and the sequential t-test.

## Use Case

A team of researchers wants to know if the stress of the employees increased in the company itFlow AG over the last year. The Human Resources department has gathered data from a new self-care tool they implemented for the employees. It is difficult to predict how many employees will participate at the second measurement point (one year after the baseline). The team suggests using a sequential t-test instead of a Students t-test, because the sequential procedure can reduce the required sample size and is stopped right after the decision for one of the hypotheses is made. Furthermore, the sequential t-test is able to gather evidence for both the alternative and the null hypothesis.

### Hypothesis

The researchers know that the company has received more orders than in the year before. Thus, they expect an increase in self-reported stress.

### Data Analysis

The parameters of the sequential t-test are specified as follows:

- The researchers expect an increase in stress but they have no prior knowledge about the expected effect size. Thus, they define the lower limit of a substantial effect of interest.

      d <- 0.2

- They choose the common \(\alpha\) level of .05 and the same level for the \(\beta\) error probability, which leads to a power of .95 (\(1 - \beta\)).

      alpha <- 0.05
      power <- 0.95

- The data are repeated measures, therefore the test is a paired sequential t-test.

      paired <- TRUE

- The researchers expect that the stress will have increased after one year. Thus, they specify that the true difference in means is greater than 0.
  `(mean(one_year_stress) - mean(baseline_stress)) > 0`

      alternative <- "greater"

The HR department receives the new data piece by piece and passes it directly on to the researchers. The test is performed for the first time and starts with the first two data points.

    # first data from the Human Resources department ---
    # current sample size
    n_person <- 2

    # get data
    df <- df_stress[1:n_person,]

    # print data
    df
    #>   baseline_stress one_year_stress
    #> 1        7.175250        7.844337
    #> 2        4.918343        5.527191

    # sequential t-test
    results <- seq_ttest(df$one_year_stress,
                         df$baseline_stress,
                         alpha = alpha,
                         power = power,
                         d = d,
                         paired = paired,
                         alternative = alternative,
                         verbose = FALSE)

    # print results: console output
    results
    #>
    #> *****  Sequential Paired t-test *****
    #>
    #> formula: df$one_year_stress and  df$baseline_stress
    #> test statistic:
    #>  log-likelihood ratio = 0.332, decision = continue sampling
    #> SPRT thresholds:
    #>  lower log(B) = -2.944, upper log(A) = 2.944

The decision from the first test is:

    results@decision
    #> [1] "continue sampling"

As a result, the researchers take one more data point and run the test again.

    # new data from the Human Resources department ---
    # get one more person
    n_person <- n_person + 1
    df <- df_stress[1:n_person,]

    # print new data
    df
    #>   baseline_stress one_year_stress
    #> 1        7.175250        7.844337
    #> 2        4.918343        5.527191
    #> 3        4.634266        5.783046

    # sequential t-test
    results <- seq_ttest(df$one_year_stress,
                         df$baseline_stress,
                         alpha = alpha,
                         power = power,
                         d = d,
                         paired = paired,
                         alternative = alternative,
                         verbose = FALSE)

    # print results
    results@decision
    #> [1] "continue sampling"

This process is repeated until the decision is made for one of the two hypotheses. To simulate this sequential process, a while-loop embraces the sequential t-test function in the code below, which will not stop until one of the hypotheses is accepted or the maximum of the data is reached.

    # define the starting point
    decision <- "continue sampling"
    n_person <- 3

    # simulation of the sequential procedure
    while(decision == "continue sampling") {
      # get the current data
      df <- df_stress[1:n_person,]

      # run the sequential test and save the results
      results <- seq_ttest(df$one_year_stress,
                           df$baseline_stress,
                           alpha = alpha,
                           power = power,
                           d = d,
                           paired = paired,
                           alternative = alternative)
      # save the current desicion
      decision <- results@decision

      # add a new person
      n_person <- n_person + 1

      # break if the maximum of the data is reached
      if (n_person > nrow(df_stress)) {
        break
      }
    }

    # console output
    results
    #>
    #> *****  Sequential Paired t-test *****
    #>
    #> formula: df$one_year_stress and  df$baseline_stress
    #> test statistic:
    #>  log-likelihood ratio = 2.988, decision = accept H1
    #> SPRT thresholds:
    #>  lower log(B) = -2.944, upper log(A) = 2.944
    #> Log-Likelihood of the:
    #>  alternative hypothesis = -2.238
    #>  null hypothesis = -5.226
    #> alternative hypothesis: true difference in means is greater than 0.
    #> specified effect size: Cohen's d = 0.2
    #> degrees of freedom: df = 47
    #> sample estimates:
    #> mean difference
    #>         0.47931
    #> *Note: to get access to the object of the results use the @ or [] instead of the $ operator.

The while-loop comes to an end after 48 data points.

### Report Results

    # Required results for the report

    # likelihood ratio (LR)
    LR <- round(results@likelihood_ratio, digits = 2)
    LR
    #> [1] 19.85

    # sample size (N) = degrees of freedom +2 (two-samples) or +1 (one-sample & paired)
    N <- results@df + 1
    N
    #> [1] 48

    # baseline stress (M and SD)
    mean_t1 <- round(mean(df$baseline_stress), digits = 2)
    mean_t1
    #> [1] 4.99
    sd_t1 <- round(sd(df$baseline_stress), digits = 2)
    sd_t1
    #> [1] 1.02

    # after one year stress (M and SD)
    mean_t2 <- round(mean(df$one_year_stress), digits = 2)
    mean_t2
    #> [1] 5.47
    sd_t2 <- round(sd(df$one_year_stress), digits = 2)
    sd_t2
    #> [1] 1.53

    # NOT INCLUDED IN THE PACKAGE

    # calculate effect size: Cohen´s d
    d_results <- effsize::cohen.d(df$one_year_stress,
                     df$baseline_stress,
                     paired = TRUE)
    d <- round(d_results$estimate, digits = 2)
    d
    #> [1] 0.34

    # confidence intervall
    d_lower <- round(d_results$conf.int[[1]], digits = 2)
    d_lower
    #> [1] 0.11
    d_upper <- round(d_results$conf.int[[2]], digits = 2)
    d_upper
    #> [1] 0.57

Starting at N = 2, the test stops sampling at a total sample size of N = 48 with LR48 = 19.85. This ratio indicates that the data are about 20 times more likely under H1 than under H0. Thus, we accept the alternative hypothesis: The perceived stress at the second measurement (M = 5.47, SD = 1.53) is higher than one year ago at the baseline measurement (M = 4.99, SD = 1.02), Cohen’s d = 0.34, 95% CI [0.11, 0.57].1,2

## References

Schnuerch, M., & Erdfelder, E. (2020). Controlling decision errors with minimal costs: The sequential probability ratio t test. Psychological Methods, 25(2), 206–226. https://doi.org/10.1037/met0000234
Schnuerch, M., Erdfelder, E., & Heck, D. W. (2020). Sequential hypothesis tests for multinomial processing tree models. Journal of Mathematical Psychology, 95, 102326. https://doi.org/10.1016/j.jmp.2020.102326

1. see Schnuerch & Erdfelder (2020).↩︎

2. Note that this estimate of Cohen’s d as well as the CI are based on the assumption of a fixed sample size and, thus, might be biased toward an overestimation of the true effect size. See for details Schnuerch et al. (2020).↩︎