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

Hao Chun Lu; Yan Xu; Tom Lu; Chun Jung Huang

doi:10.1371/journal.pone.0271163

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

Hao Chun Lu, Yan Xu^*, Tom Lu^*, Chun Jung Huang

^*Corresponding author for this work

Graduate Institute of Mangement

Research output: Contribution to journal › Journal Article › peer-review

1 Scopus citations

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.

Original language	English
Article number	e0271163
Journal	PLoS ONE
Volume	17
Issue number	8 August
DOIs	https://doi.org/10.1371/journal.pone.0271163
State	Published - 08 2022

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Copyright: © 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.

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title = "A new approach to constructing confidence intervals for population means based on small samples",

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.",

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AU - Xu, Yan

AU - Lu, Tom

AU - Huang, Chun Jung

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AB - 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.

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

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