Bootstrapping methods in computing confidence interval: Real data application

• Published in: AIP Conference Proceedings
• Main Author: Mokhtar S.F.; Yusof Z.M.; Sapiri H.
• Format: Conference paper
• Language: English
• Published: American Institute of Physics Inc. 2023
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85179827096&doi=10.1063%2f5.0177188&partnerID=40&md5=c619c62cbc3203d445513dd7e67d08db

Violation of standard assumptions in traditional method will create problem especially when estimating the confidence intervals. In the realm of applied statistics, bootstrapping methods have achieved widespread recognition and application. Bootstrapping is a useful approach to estimate standard errors and also to obtain confidence intervals for location parameters. Bootstrapping is a computationally costly statistical approach that allows researchers to draw conclusions from the data without the need to fulfil the assumptions. The purpose of this study is to present the steps involved in calculating the confidence interval using the bootstrapping methods and to discuss on the findings. This paper also compares steps in traditional confidence interval, normal interval, percentile bootstrap and bootstrap-t methods by using real dataset. The result reveals that the normal interval outperformed the other methods as it produced the smallest interval length. Bootstrapping does not need assumptions about the distribution of the data. Bootstrapping is also a useful method because it relaxed from normality, independence, and constants variation assumptions. © 2023 Author(s).