Papers › Computation of Reliability Statistics for Success-Failure Experiments
Computation of Reliability Statistics for Success-Failure Experiments
Sanjay M. Joshi
The archive published only this paper's code-link row. Authors, date and abstract are from arXiv's metadata (CC0), read from the Kaggle arXiv metadata snapshot of 2026-09-12 where its title matched the archive's; the title is the archive's.
Reliability is probability of success in a success-failure experiment. Confidence in reliability estimate improves with increasing number of samples. Assurance sets confidence level same as reliability to create one number for easier communication. Assuming binomial distribution for the samples, closed-form expression exists only for calculating confidence. The Wilson Score interval with continuity correction provides approximate closed-form expression for reliability. Brent's method was found to provide fast and accurate estimate for both reliability and assurance computations. Graphs and tables are provided for several number of samples. Two open-source python libraries are introduced for computing reliability, confidence, and assurance.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections