Related Experiment Video
Updated: Dec 26, 2025

Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study
Published on: January 31, 2014
Exact critical values for group sequential designs with small sample sizes
Dror M Rom1, Jaclyn A McTague1
1Department of Statistics, Logecal Data Analytics , Broomall, Pennsylvania, USA.
Abstract:
Group sequential clinical trial designs allow the sequential hypothesis testing as data is accumulated over time, while ensuring the control of type-1 error rate. These designs vary in how they split the overall type-1 error among analyses, but practically, all assume that: 1. The underlying data is normal or approximately so, and 2. the sample sizes are large, so the individual test statistics are sufficiently normal rather than Student's t. These two assumptions lead to the reliance on the multivariate normal distribution for calculation of the critical values. Several publications have pointed out that for small sample sizes, such an approach leads to an inflated type-1 error and proposed different sets of critical values from either simulations or by an ad-hoc adjustment to the asymptotic critical values. In this paper, we develop the exact joint distribution of the test statistics for any sample size. We show how to calculate exact critical values that conform to some well-known alpha-spending functions, such as the O'Brien-Fleming and Pocock critical values. We also compare the resulting type-1 error of these critical values with the asymptotic, as well as with other methods that have been proposed for small sample sizes.
More Related Videos
08:58Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow
Published on: October 17, 2025
11:58The Replica Set Method: A High-throughput Approach to Quantitatively Measure Caenorhabditis elegans Lifespan
Published on: June 29, 2018
Related Concept Videos
Finding Critical Values for Chi-Square
One-Way ANOVA: Equal Sample Sizes
Different sample means can result in different values for the variance estimate: variance between samples. This is because the variance between samples is calculated as the product of the sample size and the variance between the...
Critical Region, Critical Values and Significance Level
In hypothesis testing, a sample statistic is converted to a test statistic using z, t, or chi-square distribution. A critical region is an area under the curve in probability distributions demarcated by the critical value. When the test statistic falls in this region, it suggests that the null hypothesis must be rejected. As this region contains all those values of the...
Critical Values
Bonferroni Test
The means of different samples are first paired in all possible combinations.
The null hypothesis of the...
Decision Making: Traditional Method
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...