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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| Format: | Article |
| Language: | English |
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BMC
2021-12-01
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| Series: | Emerging Themes in Epidemiology |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12982-021-00108-1 |
| _version_ | 1818344515177545728 |
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| author | Sonja Hartnack Malgorzata Roos |
| author_facet | Sonja Hartnack Malgorzata Roos |
| author_sort | Sonja Hartnack |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-13T16:47:42Z |
| format | Article |
| id | doaj.art-9036f3283b16491db8490c1e82bf8665 |
| institution | Directory Open Access Journal |
| issn | 1742-7622 |
| language | English |
| last_indexed | 2024-12-13T16:47:42Z |
| publishDate | 2021-12-01 |
| publisher | BMC |
| record_format | Article |
| series | Emerging Themes in Epidemiology |
| spelling | doaj.art-9036f3283b16491db8490c1e82bf86652022-12-21T23:38:06ZengBMCEmerging Themes in Epidemiology1742-76222021-12-0118111410.1186/s12982-021-00108-1Teaching: confidence, prediction and tolerance intervals in scientific practice: a tutorial on binary variablesSonja Hartnack0Malgorzata Roos1Section of Epidemiology, Vetsuisse Faculty, University of ZurichEpidemiology, Biostatistics and Prevention Institute, University of ZurichAbstract 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.https://doi.org/10.1186/s12982-021-00108-1Statistical interval estimatesRandom sampleBayesian analysisJeffreys prior |
| spellingShingle | Sonja Hartnack Malgorzata Roos Teaching: confidence, prediction and tolerance intervals in scientific practice: a tutorial on binary variables Emerging Themes in Epidemiology Statistical interval estimates Random sample Bayesian analysis Jeffreys prior |
| title | 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_short | 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 |
| topic | Statistical interval estimates Random sample Bayesian analysis Jeffreys prior |
| url | https://doi.org/10.1186/s12982-021-00108-1 |
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