A new approach to constructing confidence intervals for population means based on small samples

Date

2022

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Abstract

This paper presents a new approach to constructing the confidence interval for the mean value of a population when the distribution is unknown and the sample size is small, called the Percentile Data Construction Method (PDCM). A simulation was conducted to compare the performance of the PDCM confidence interval with those generated by the Percentile Bootstrap (PB) and Normal Theory (NT) methods. Both the convergence probability and average interval width criterion are considered when seeking to find the best interval. The results show that the PDCM outperforms both the PB and NT methods when the sample size is less than 30 or a large population variance exists.

Description

ght: © 2022 Lu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Keywords

Percentile Data Construction Method (PDCM), Simulation, Percentile Bootstrap (PB), Normal Theory (NT), convergence probability

Citation

Article Source: A new approach to constructing confidence intervals for population means based on small samples Lu HC, Xu Y, Lu T, Huang CJ (2022) A new approach to constructing confidence intervals for population means based on small samples. PLOS ONE 17(8): e0271163. https://doi.org/10.1371/journal.pone.0271163

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