Name:
Predictive Inference and Scientific ...
Size:
966.8Kb
Format:
PDF
Description:
Final Published Version
Author
Billheimer, DeanAffiliation
Univ Arizona, Dept Biostat & EpidemiolIssue Date
2019
Metadata
Show full item recordPublisher
AMER STATISTICAL ASSOCCitation
Billheimer, D. (2019). Predictive Inference and Scientific Reproducibility. The American Statistician, 73(sup1), 291-295.Journal
AMERICAN STATISTICIANRights
© 2019 The Author. Published with license by Taylor & Francis Group, LLC. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/).Collection Information
This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.Abstract
Most statistical analyses use hypothesis tests or estimation about parameters to form inferential conclusions. I think this is noble, but misguided. The point of view expressed here is that observables are fundamental, and that the goal of statistical modeling should be to predict future observations, given the current data and other relevant information. Further, the prediction of future observables provides multiple advantages to practicing scientists, and to science in general. These include an interpretable numerical summary of a quantity of direct interest to current and future researchers, a calibrated prediction of what's likely to happen in future experiments, a prediction that can be either "corroborated" or "refuted" through experimentation, and avoidance of inference about parameters; quantities that exists only as convenient indices of hypothetical distributions. Finally, the predictive probability of a future observable can be used as a standard for communicating the reliability of the current work, regardless of whether confirmatory experiments are conducted. Adoption of this paradigm would improve our rigor for scientific accuracy and reproducibility by shifting our focus from "finding differences" among hypothetical parameters to predicting observable events based on our current scientific understanding.Note
Open access articleISSN
0003-1305Version
Final published versionae974a485f413a2113503eed53cd6c53
10.1080/00031305.2018.1518270
Scopus Count
Collections
Except where otherwise noted, this item's license is described as © 2019 The Author. Published with license by Taylor & Francis Group, LLC. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/).