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

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 type...

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Autores principales:	Sonja Hartnack, Malgorzata Roos
Formato:	article
Lenguaje:	EN
Publicado:	BMC 2021
Materias:	Statistical interval estimates Random sample Bayesian analysis Jeffreys prior Infectious and parasitic diseases RC109-216
Acceso en línea:	https://doaj.org/article/9036f3283b16491db8490c1e82bf8665
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spelling	oai:doaj.org-article:9036f3283b16491db8490c1e82bf86652021-12-05T12:20:37ZTeaching: confidence, prediction and tolerance intervals in scientific practice: a tutorial on binary variables10.1186/s12982-021-00108-11742-7622https://doaj.org/article/9036f3283b16491db8490c1e82bf86652021-12-01T00:00:00Zhttps://doi.org/10.1186/s12982-021-00108-1https://doaj.org/toc/1742-7622Abstract 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.Sonja HartnackMalgorzata RoosBMCarticleStatistical interval estimatesRandom sampleBayesian analysisJeffreys priorInfectious and parasitic diseasesRC109-216ENEmerging Themes in Epidemiology, Vol 18, Iss 1, Pp 1-14 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Statistical interval estimates Random sample Bayesian analysis Jeffreys prior Infectious and parasitic diseases RC109-216
spellingShingle	Statistical interval estimates Random sample Bayesian analysis Jeffreys prior Infectious and parasitic diseases RC109-216 Sonja Hartnack Malgorzata Roos Teaching: confidence, prediction and tolerance intervals in scientific practice: a tutorial on binary variables
description	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.
format	article
author	Sonja Hartnack Malgorzata Roos
author_facet	Sonja Hartnack Malgorzata Roos
author_sort	Sonja Hartnack
title	Teaching: confidence, prediction and tolerance intervals in scientific practice: a tutorial on binary variables
title_short	Teaching: confidence, prediction and tolerance intervals in scientific practice: a tutorial on binary variables
title_full	Teaching: confidence, prediction and tolerance intervals in scientific practice: a tutorial on binary variables
title_fullStr	Teaching: confidence, prediction and tolerance intervals in scientific practice: a tutorial on binary variables
title_full_unstemmed	Teaching: confidence, prediction and tolerance intervals in scientific practice: a tutorial on binary variables
title_sort	teaching: confidence, prediction and tolerance intervals in scientific practice: a tutorial on binary variables
publisher	BMC
publishDate	2021
url	https://doaj.org/article/9036f3283b16491db8490c1e82bf8665
work_keys_str_mv	AT sonjahartnack teachingconfidencepredictionandtoleranceintervalsinscientificpracticeatutorialonbinaryvariables AT malgorzataroos teachingconfidencepredictionandtoleranceintervalsinscientificpracticeatutorialonbinaryvariables
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Teaching: confidence, prediction and tolerance intervals in scientific practice: a tutorial on binary variables

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