Sequential t-Test

Meike Steinhilber



The sprtt package is a sequential probability ratio tests toolbox (sprtt). This vignette describes the theoretical background of these tests.

Other recommended vignettes cover:

What is a sequential test procedure?

With a sequential approach, data is continuously collected and an analysis is performed after each data point, which can lead to three different results (A. Wald, 1945):

Basically it is not necessary to perform an analysis after each data point β€” several data points can also be added at once. However, this affects the sample size (N) and the error rates (Schnuerch et al., 2020).

The efficiency of sequential designs has already been examined. Reductions in the sample by 50% and more were found in comparison to analyses with fixed sample sizes (Schnuerch et al., 2020; A. Wald, 1945). Sequential hypothesis testing is therefore particularly suitable when resources are limited because the required sample size is reduced without compromising predefined error probabilities.

What is the sequential t-test?

The sequential t-test is based on the Sequential Probability Ratio Test (SPRT) by Abraham Abraham Wald (1947), which is a highly efficient sequential hypothesis test. However, the usage of WaldΒ΄s SPRT is limited in the case of normally distributed data, because the variance has to be known or specified in the hypothesis. Rushton (1950; 1952) and Hajnal (1961) have further developed the SPRT using the t-statistic. The basic idea is to transform the sequence of observations (which is dependent on the variance) into a sequence of the associated t-statistic (which is independent of the variance).

In the SPRT the null and alternative hypotheses are defined as follows, with πœƒ representing the model parameter :

\[ H_0:\ πœƒ\ =\ πœƒ_0 \\ H_1:\ πœƒ\ =\ πœƒ_1 \]

The test statistic of the SPRT is based on a likelihood ratio, which is a measure of the relative evidence in the data for the given hypotheses. More specifically, it is the ratio of the likelihood of the alternative hypothesis to the likelihood of the null hypothesis at the m-th step of the sampling process (LRm).

\[ LR_{m} = \frac {f(data_m | H_1)} {f(data_m | H_0)} = \frac {𝑓(x_1,...,x_m | πœƒ_1)} {𝑓(x_1,...,x_m | πœƒ_0)} \]

Before the transformation into the t-statistic, the model parameter πœƒ contains the parameters of a normal distribution: the mean (Β΅) and the standard deviation (𝜎). Therefore, the Wald SPRT requires prior knowledge about the variance (𝜎2) or a specification in the hypotheses.

After the transformation of the observed values into the associated t-statistic, the model parameter πœƒ contains the parameters of the non-central t-distribution: the degrees of freedom (df) and the non-centrality parameter (π›₯).

\[ {𝑓(x_1,...,x_m | Β΅,𝜎)} => {𝑓(t_2,...,t_m | df,π›₯)} \]

For the calculation of the degrees of freedom, only the sample size of the group(s) is needed. The non-centrality parameter also requires a specification of the expected effect size in form of Cohen`s d (d).

To eventually calculate the LR of the sequential t-test, only the current tm-statistic is necessary. S. Rushton (1950) demonstrated that an SPRT can be performed by simply considering the ratio of probability densities for the most recent tm statistic under the alternative and null hypothesis at any m-th stage. Thus, the test statistic for a one and two-sided sequential t-test can be calculated as follows:

\[ LR_{m,\ one-sided\ sequential\ t-test} = \frac {𝑓(t_m | πœƒ_1)} {𝑓(t_m | πœƒ_0)} \\ LR_{m,\ two-sided\ sequential\ t-test} = \frac {𝑓(t_m^2 | πœƒ_1)} {𝑓(t_m^2 | πœƒ_0)}. \]

To account for the fact that the algebraic sign is unknown in a two-sided test, the t-value is squared (S. Rushton, 1952).

After the calculation of the test statistic, the decision will be either to continue sampling or to terminate the sampling and accept one of the hypotheses. A. Wald (1945) defined the following rules for the SPRT:

Condition Decision
LRm ≀ B accept H0 and reject H1
B < LRm < A continue sampling
LRm ≀ A accept H1 and reject H0

The A and B boundaries are calculated with the previously defined error rates 𝛼 (Type I error) and 𝛽 (Type II error) as follows:

\[ A = \left( \frac{1 - 𝛽}{𝛼} \right) \\ B = \left( \frac{𝛽}{1 - 𝛼} \right). \]

In summary, three specifications are required to calculate a sequential t-test:


Hajnal, J. (1961). A two-sample sequential t-test. Biometrika, 48(1/2), 65–75.
Rushton, S. (1950). On a sequential t-test. Biometrika, 37, 326–333.
Rushton, S. (1952). On a Two-Sided Sequential t-Test. Biometrika, 39(3/4), 302.
Schnuerch, M., Erdfelder, E., & Heck, D. W. (2020). Sequential hypothesis tests for multinomial processing tree models. Journal of Mathematical Psychology, 95, 102326.
Wald, A. (1945). Sequential tests of statistical hypotheses. The Annals of Mathematical Statistics, 16(2), 117–186.
Wald, Abraham. (1947). Sequential analysis. Wiley.