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Teaching: confidence, prediction and tolerance intervals in scientific practice: a tutorial on binary variables


Hartnack, Sonja; Roos, Malgorzata (2021). Teaching: confidence, prediction and tolerance intervals in scientific practice: a tutorial on binary variables. Emerging Themes in Epidemiology, 18(1):17.

Abstract

Background: One of the emerging themes in epidemiology is the use of interval estimates. Currently, three interval estimates for confidence (CI), prediction (PI), and tolerance (TI) are at a researcher's disposal and are accessible within the open access framework in R. These three types of statistical intervals serve different purposes. Confidence intervals are designed to describe a parameter with some uncertainty due to sampling errors. Prediction intervals aim to predict future observation(s), including some uncertainty present in the actual and future samples. Tolerance intervals are constructed to capture a specified proportion of a population with a defined confidence. It is well known that interval estimates support a greater knowledge gain than point estimates. Thus, a good understanding and the use of CI, PI, and TI underlie good statistical practice. While CIs are taught in introductory statistical classes, PIs and TIs are less familiar.
Results: In this paper, we provide a concise tutorial on two-sided CI, PI and TI for binary variables. This hands-on tutorial is based on our teaching materials. It contains an overview of the meaning and applicability from both a classical and a Bayesian perspective. Based on a worked-out example from veterinary medicine, we provide guidance and code that can be directly applied in R.
Conclusions: This tutorial can be used by others for teaching, either in a class or for self-instruction of students and senior researchers.

Abstract

Background: One of the emerging themes in epidemiology is the use of interval estimates. Currently, three interval estimates for confidence (CI), prediction (PI), and tolerance (TI) are at a researcher's disposal and are accessible within the open access framework in R. These three types of statistical intervals serve different purposes. Confidence intervals are designed to describe a parameter with some uncertainty due to sampling errors. Prediction intervals aim to predict future observation(s), including some uncertainty present in the actual and future samples. Tolerance intervals are constructed to capture a specified proportion of a population with a defined confidence. It is well known that interval estimates support a greater knowledge gain than point estimates. Thus, a good understanding and the use of CI, PI, and TI underlie good statistical practice. While CIs are taught in introductory statistical classes, PIs and TIs are less familiar.
Results: In this paper, we provide a concise tutorial on two-sided CI, PI and TI for binary variables. This hands-on tutorial is based on our teaching materials. It contains an overview of the meaning and applicability from both a classical and a Bayesian perspective. Based on a worked-out example from veterinary medicine, we provide guidance and code that can be directly applied in R.
Conclusions: This tutorial can be used by others for teaching, either in a class or for self-instruction of students and senior researchers.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:04 Faculty of Medicine > Epidemiology, Biostatistics and Prevention Institute (EBPI)
05 Vetsuisse Faculty > Veterinärwissenschaftliches Institut > Chair in Veterinary Epidemiology
Dewey Decimal Classification:570 Life sciences; biology
610 Medicine & health
Scopus Subject Areas:Health Sciences > Epidemiology
Uncontrolled Keywords:Epidemiology
Language:English
Date:1 December 2021
Deposited On:17 Dec 2021 14:33
Last Modified:27 Jan 2024 02:45
Publisher:BioMed Central
ISSN:1742-7622
OA Status:Gold
Free access at:Publisher DOI. An embargo period may apply.
Publisher DOI:https://doi.org/10.1186/s12982-021-00108-1
  • Content: Published Version
  • Language: English
  • Licence: Creative Commons: Attribution 4.0 International (CC BY 4.0)