Editing 2323: Modeling Study

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Randall doesn't call this a [[:Category:Tips|"tip"]], but it does fit in with his [[:Category:Science tip|science tip]] in [[2311: Confidence Interval]], namely, that "If your model is bad enough, the confidence intervals will fall outside the printable area."  Much as that tip suggests that a model's results can be made to look more impressive by hiding the error bounds outside the printed area of a graph, this comic strip suggests that acknowledgments of problems can be moved to less-trafficked parts of the paper by switching from empirical to modeling studies.
 
Randall doesn't call this a [[:Category:Tips|"tip"]], but it does fit in with his [[:Category:Science tip|science tip]] in [[2311: Confidence Interval]], namely, that "If your model is bad enough, the confidence intervals will fall outside the printable area."  Much as that tip suggests that a model's results can be made to look more impressive by hiding the error bounds outside the printed area of a graph, this comic strip suggests that acknowledgments of problems can be moved to less-trafficked parts of the paper by switching from empirical to modeling studies.
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For a more concrete example, consider the 2020 pandemic of COVID-19.  Empirical studies measure things like infections, hospitalizations, and deaths, and the circumstances that lead to those events, and attempt to answer questions about how COVID-19 spreads, what measures are effective in preventing its transmission, what those measures' other costs and side effects are, and what therapies are effective in treating cases.  These are made difficult by gaps in testing capability, the imperfections of those tests which are available, and the fact that all of the conditions of society are interconnected and constantly changing -- there is no "control universe" or any way to go back and try different ideas.  Modeling studies offer the possibility to simulate thousands or millions of possible pandemics, to hopefully figure out those variables' effects in advance and offer guidance to governments and health workers, but without specific knowledge of COVID-19's properties. Especially in the early days of the pandemic, modelers must make assumptions about how COVID-19 spreads, kills, and is (or is not) treated.  For pandemics especially, which behave exponentially until they are brought under control (or the pathogen burns through its host population), even small changes in model assumptions can lead to orders of magnitude difference between equally-plausible projections (such as [https://www.newscientist.com/article/2238578-uk-has-enough-intensive-care-units-for-coronavirus-expert-predicts/ projected deaths falling from half a million to 20,000]; note that all of these outcomes are [https://www.abs.gov.au/websitedbs/a3121120.nsf/home/statistical+language+-+estimate+and+projection ''projections'' rather than ''predictions''], even though they are often presented as the latter in the press).  Even if all such projections are made earnestly, with the best available information, it can lead to distrust of the models and their results, especially if the models are presented to non-experts with too much certainty. Moreover, projections which attempt to project how many deaths will happen if nothing is done to stop an outbreak, and which are therefore more dire than those which simply assume that protective actions will be taken, are self-defeating ''specifically because'' they cause people to take the outbreak seriously and take protective actions.
  
 
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