Quantifying uncertainty : all we need is the bootstrap?

Zrimšek, Urša (author), Štrumbelj, Erik (author)

{A critical literature review and comprehensive simulation study is used to show that (a) non-parametric bootstrap is a viable alternative to commonly taught and used methods in basic estimation tasks (mean, variance, quartiles, correlation). and (b) contrary to recommendations in most related work, double bootstrap performs better than BCa.} Quantifying uncertainty is a fundamental aspect of statistical practice, but it involves a variety of methods, mathematical formulas, and underlying concepts. Could the simpler and more generally applicable non-parametric bootstrap serve as an alternative? This paper addresses this question through a review of related work and a simulation study of one- and two-sided confidence intervals across varying sample sizes, confidence levels, data-generating processes, and statistical functionals. The results suggest that the bootstrap, particularly the double bootstrap, could simplify statistical education and practice without compromising effectiveness.

• Language: English
• Keywords: statistics, inference, standard errors, confidence intervals, simulation study
• Work type: Article
• Typology: 1.01 - Original Scientific Article
• Organization: FRI - Faculty of Computer and Information Science
• Publication status: Published
• Publication version: Version of Record
• Year: 2026
• Number of pages: Str. 1009–1027
• Numbering: Vol. 96, no. 4
• PID: 20.500.12556/RUL-175851
• UDC: 519.237:004
• ISSN on article: 0094-9655
• DOI: 10.1080/00949655.2025.2577274
• COBISS.SI-ID: 255169027
• Publication date in RUL: 11.11.2025

Record is a part of a journal

• Title: Journal of statistical computation and simulation
• Shortened title: J. stat. comput. simul.
• Publisher: Taylor & Francis
• ISSN: 0094-9655
• COBISS.SI-ID: 25793024

Licences

• License: CC BY-NC-ND 4.0, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
• Link: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Description: The most restrictive Creative Commons license. This only allows people to download and share the work for no commercial gain and for no other purposes.

Secondary language

• Language: Slovenian
• Keywords: statistika, statistično sklepanje, standardne napake, intervali zaupanja, simulacijska študija

Projects

• Funder: ARIS - Slovenian Research and Innovation Agency
• Project number: P2-0442
• Name: Podatkovne vede in digitalna preobrazba

• Funder: ARIS - Slovenian Research and Innovation Agency
• Project number: J5-60084
• Name: Upravljanje umetne inteligence: povezovanje razložljive in generativne umetne inteligence - izzivi in priložnosti za upravljanje znanja v organizacijah