Related Experiment Video
Updated: Jun 28, 2025

Rare Event Detection Using Error-corrected DNA and RNA Sequencing
Published on: August 3, 2018
An exhaustive ADDIS principle for online FWER control
Lasse Fischer1, Marta Bofill Roig2, Werner Brannath1
1Competence Center for Clinical Trials Bremen, University of Bremen, Bremen, Germany.
Abstract:
In this paper, we consider online multiple testing with familywise error rate (FWER) control, where the probability of committing at least one type I error will remain under control while testing a possibly infinite sequence of hypotheses over time. Currently, adaptive-discard (ADDIS) procedures seem to be the most promising online procedures with FWER control in terms of power. Now, our main contribution is a uniform improvement of the ADDIS principle and thus of all ADDIS procedures. This means, the methods we propose reject as least as much hypotheses as ADDIS procedures and in some cases even more, while maintaining FWER control. In addition, we show that there is no other FWER controlling procedure that enlarges the event of rejecting any hypothesis. Finally, we apply the new principle to derive uniform improvements of the ADDIS-Spending and ADDIS-Graph.
Related Concept Videos
Propagation of Uncertainty from Random Error
Propagation of Uncertainty from Systematic Error
Accuracy and Errors in Hypothesis Testing
In hypothesis testing, the probability of making a Type I error, denoted as α, is commonly set at 0.05. This significance level indicates a 5%...
Types of Errors: Detection and Minimization
Absolute error in a measurement is the numerical difference from the true or central value. Relative error is the ratio between absolute error and the true or central value, expressed as a percentage.
Errors can be classified by source, magnitude, and sign. There are three types of errors: systematic, random, and gross.
Systematic or...
Contaminants and Errors
Another key consideration is determining the appropriate number of samples required to...
Systematic Error: Methodological and Sampling Errors
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...

