# Sample size estimation for power and accuracy in the experimental comparison of algorithms

arXiv: https://arxiv.org/abs/1808.02997 · HTML: https://mlsanity.com/paper/1808.02997/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Aug 2018.

## Built on by (most cited first, 1 of 1)

- [Learning to Assess the Reliability of Number-of-Runs Estimation in Stochastic Optimization](https://mlsanity.com/paper/2605.28309/) (2026)

## Abstract (arXiv)

Experimental comparisons of performance represent an important aspect of research on optimization algorithms. In this work we present a methodology for defining the required sample sizes for designing experiments with desired statistical properties for the comparison of two methods on a given problem class. The proposed approach allows the experimenter to define desired levels of accuracy for estimates of mean performance differences on individual problem instances, as well as the desired statistical power for comparing mean performances over a problem class of interest. The method calculates the required number of problem instances, and runs the algorithms on each test instance so that the accuracy of the estimated differences in performance is controlled at the predefined level. Two examples illustrate the application of the proposed method, and its ability to achieve the desired statistical properties with a methodologically sound definition of the relevant sample sizes.