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Modelling Goals, Strategies, and Mistakes

William Briggs

Chapter Chapter 10 in Uncertainty, 2016, pp 203-244 from Springer

Abstract: Abstract Here are highlighted only a few of the most egregious and common mistakes made in modeling. Particular models are not emphasized so much as how model results should be communicated. The goal of probability models is to quantify uncertainty in an observable Y given assumptions or observations X. That and nothing more. This, and only this, form of model result should be presented. Regression is of paramount importance. The horrors to thought and clear reasoning committed in its name are legion. Scarcely any user of regression knows its limitations, mainly because of the fallacies of hypothesis testing and the over-certainty of parameter-based reporting. The Deadly Sin of Reification is detailed. The map is not the territory, though this fictional land is unfortunately where many choose to live. When the data do not match a theory, it is often the data that are suspected, not the theory. Models should never take the place of actual data, though they often do, particularly in time series. Risk is nearly always exaggerated. The fallacious belief that we can quantify the unquantifiable is responsible for scientism. “Smoothed” data is often given pride of place over actual observations. Over-certainty rampages across the land and leads to irreproducible results.

Keywords: Time Series; Deterministic Model; Exposed Group; Prediction Limit; Urban Warming (search for similar items in EconPapers)
Date: 2016
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-319-39756-6_10

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DOI: 10.1007/978-3-319-39756-6_10

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