# Introduction to SPRTs

#### Meike Snijder-Steinhilber

#### 2026-05-06

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

If you are interested in a workflow to use the `sprtt` package, see the vignette `vignette("workflow_sprtt")`. For a simple use case of a sequential t-test see the vignette `vignette("use_case")`.

### The Sequential Testing Principle

Sequential Probability Ratio Tests (SPRTs) fundamentally differ from fixed-sample designs by continuously evaluating evidence as data accumulates (Wald, 1945). After collecting each data point (or batch of data points), the test leads to one of three outcomes:

- Continue sampling: Evidence remains inconclusive

- Stop and accept \(H_0\) (no effect): Sufficient evidence accumulated against an effect

- Stop and accept \(H_1\) (effect): Sufficient evidence accumulated for an effect

This approach allows researchers to stop data collection as soon as sufficient evidence has been obtained, leading to substantial efficiency gains compared to fixed-sample designs.

### The Likelihood Ratio and Decision Boundaries

At the core of SPRTs is the likelihood ratio \(\text{LR}_n\), which quantifies the relative evidence for \(H_1\) versus \(H_0\) after \(n\) observations:

\[\text{LR}_n = \frac{\text{L}_n(H_1)}{\text{L}_n(H_0)} = \frac{f(\text{data}_n \mid H_1)}{f(\text{data}_n \mid H_0)}\]

If you are unfamiliar with the concept of likelihood, we recommend the paper by Etz (2018).

The SPRT compares the likelihood ratio to two boundaries (\(A\) and \(B\)), and the following rules apply:

Condition | Data collection | Hypothesis
--- | --- | ---
\(LR_m \leq B\) | Stop data collection | Accept \(H_0\) and reject \(H_1\)
\(B < LR_m < A\) | Continue sampling | No decision is made (yet)
\(LR_m \geq A\) | Stop data collection | Accept \(H_1\) and reject \(H_0\)

These boundaries are determined by the desired Type I (\(\alpha\)) and Type II (\(\beta\)) error rates:

\[A = \frac{1-\beta}{\alpha} \quad \text{and} \quad B = \frac{\beta}{1-\alpha}\]

In practice, it is often more convenient to work with the log-likelihood ratio \(\text{LLR}_n = \log(\text{LR}_n)\). The logarithm transforms products of likelihoods into sums, which improves numerical stability (avoiding very small or very large numbers) and simplifies computation.

The corresponding log-boundaries are:

\[\text{LLR}_n \geq \log(A) \rightarrow \text{accept } H_1\]

\[\text{LLR}_n \leq \log(B) \rightarrow \text{accept } H_0\]

The `sprtt` package uses the \(\text{LLR}_n\) for the internal calculations.

### Random Sample Size

Unlike fixed-sample designs where \(N\) is predetermined, the sample size in SPRTs is a random variable. You don’t know beforehand when the test will stop. When the SPRT will stop depends on:

- Random variation in the observed data
- The true effect size in the population
- The effect size specified under \(H_1\)
- The specified error rates (\(\alpha\), \(\beta\))

When the true effect matches \(H_1\) (or \(H_0\)), the test tends to stop quickly. When the truth lies between the hypotheses, stopping may take longer. This randomness is a feature, not a bug – it’s what enables the efficiency gains.

### Why SPRTs Always Stop

A crucial theoretical property of SPRTs is that they are guaranteed to terminate with probability 1 under both \(H_0\) and \(H_1\) (Wald, 1947). This means that if you continue collecting data, the likelihood ratio will eventually cross one of the boundaries – you won’t collect data indefinitely.

The mathematical proof relies on the law of large numbers and properties of random walks. However, while termination is guaranteed asymptotically, practical constraints (budget, time) may require setting a maximum sample size \(N_{\text{max}}\) based on available resources. The `plan_sample_size()` function helps determine the required \(N_{\text{max}}\) for a given design, ensuring a desired decision rate (e.g., 80%) is achieved, that is, the test reaches a decision before exhausting resources in at least 80% of cases.

### Efficiency

SPRTs achieve remarkable efficiency compared to fixed-sample designs (Wald, 1945). On average, SPRTs require approximately 58% fewer observations to reach the same decision with the same error rates (Steinhilber et al., 2024).

Additionally, SPRTs are especially efficient compare to fixed-sample designs, when the expected effect (or effect size of interest) is small and when the expected effect is smaller than the true effect in the data (Steinhilber et al., 2024).

### The Bias-Efficiency Tradeoff

While SPRTs are highly efficient, they come with an important caveat: effect size estimates are conditionally biased (Schnuerch & Erdfelder, 2020; Steinhilber et al., 2024).

Small samples (early stops): lead to overestimation of effect sizes in single studies.

Large samples (late stops): lead to underestimation of effect sizes in single studies.

However, across multiple studies, the weighted average of effect size estimates is close to the true population parameter.

This means that effect size estimates from individual SPRT studies should be interpreted with caution. It is important to note that conditional bias of sequential samples is not specific to SPRTs but a general phenomenon of sequential testing procedures (Fan et al., 2004; Nardini & Sprenger, 2013; Whitehead, 1986).

Practical implications:

- Use SPRTs for hypothesis testing (accept/reject decisions)
- Be cautious when interpreting point estimates from individual sequential studies
- For precise effect size estimation, consider fixed-sample designs with large enough samples or bias-corrected estimators, where available

### Practical Considerations

While SPRTs theoretically continue until a boundary is crossed, practical constraints require planning. The `sprtt` package provides tools to explore these tradeoffs through the `plan_sample_size()` function, helping you find designs that balance efficiency with feasibility.

### References

Etz, A. (2018). Introduction to the concept of likelihood and its applications. Advances in Methods and Practices in Psychological Science, 1(1), 60–69. https://doi.org/10.1177/2515245917744314
Fan, X. (Frank)., DeMets, D. L., & Lan, K. K. G. (2004). Conditional bias of point estimates following a group sequential test. Journal of Biopharmaceutical Statistics, 14(2), 505–530. https://doi.org/10.1081/BIP-120037195
Nardini, C., & Sprenger, J. (2013). Bias and Conditioning in Sequential Medical Trials. Philosophy of Science, 80(5), 1053–1064. https://doi.org/10.1086/673732
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
Steinhilber, M., Schnuerch, M., & Schubert, A.-L. (2024). Sequential analysis of variance: Increasing efficiency of hypothesis testing. Psychological Methods. https://doi.org/10.1037/met0000677
Wald, A. (1945). Sequential tests of statistical hypotheses. The Annals of Mathematical Statistics, 16(2), 117–186.
Wald, A. (1947). Sequential analysis. Wiley.
Whitehead, J. (1986). On the bias of maximum likelihood estimation following a sequential test. Biometrika, 73(3), 573–581.