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      Explanation of observational data engenders a causal belief about smoking and cancer

      research-article
      1 , , 2 , 2
      PeerJ
      PeerJ Inc.
      Inference, Behavior, Causality, Randomized trial, Interpretation, Explanation

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          Abstract

          Most researchers do not deliberately claim causal results in an observational study. But do we lead our readers to draw a causal conclusion unintentionally by explaining why significant correlations and relationships may exist? Here we perform a randomized controlled experiment in a massive open online course run in 2013 that teaches data analysis concepts to test the hypothesis that explaining an analysis will lead readers to interpret an inferential analysis as causal. We test this hypothesis with a single example of an observational study on the relationship between smoking and cancer. We show that adding an explanation to the description of an inferential analysis leads to a 15.2% increase in readers interpreting the analysis as causal (95% confidence interval for difference in two proportions: 12.8%–17.5%). We then replicate this finding in a second large scale massive open online course. Nearly every scientific study, regardless of the study design, includes an explanation for observed effects. Our results suggest that these explanations may be misleading to the audience of these data analyses and that qualification of explanations could be a useful avenue of exploration in future research to counteract the problem. Our results invite many opportunities for further research to broaden the scope of these findings beyond the single smoking-cancer example examined here.

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          The association between exaggeration in health related science news and academic press releases: retrospective observational study

          To identify the source (press releases or news) of distortions, exaggerations, or changes to the main conclusions drawn from research that could potentially influence a reader's health related behaviour.
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            Graphical perception and graphical methods for analyzing scientific data.

            Graphical perception is the visual decoding of the quantitative and qualitative information encoded on graphs. Recent investigations have uncovered basic principles of human graphical perception that have important implications for the display of data. The computer graphics revolution has stimulated the invention of many graphical methods for analyzing and presenting scientific data, such as box plots, two-tiered error bars, scatterplot smoothing, dot charts, and graphing on a log base 2 scale.
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              The Bradford Hill considerations on causality: a counterfactual perspective

              Bradford Hill's considerations published in 1965 had an enormous influence on attempts to separate causal from non-causal explanations of observed associations. These considerations were often applied as a checklist of criteria, although they were by no means intended to be used in this way by Hill himself. Hill, however, avoided defining explicitly what he meant by "causal effect". This paper provides a fresh point of view on Hill's considerations from the perspective of counterfactual causality. I argue that counterfactual arguments strongly contribute to the question of when to apply the Hill considerations. Some of the considerations, however, involve many counterfactuals in a broader causal system, and their heuristic value decreases as the complexity of a system increases; the danger of misapplying them can be high. The impacts of these insights for study design and data analysis are discussed. The key analysis tool to assess the applicability of Hill's considerations is multiple bias modelling (Bayesian methods and Monte Carlo sensitivity analysis); these methods should be used much more frequently.
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                Author and article information

                Contributors
                Journal
                PeerJ
                PeerJ
                peerj
                peerj
                PeerJ
                PeerJ Inc. (San Francisco, USA )
                2167-8359
                12 September 2018
                2018
                : 6
                : e5597
                Affiliations
                [1 ]Department of Mathematics, Statistics, and Computer Science, Macalester College , St. Paul, MN, United States of America
                [2 ]Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health , Baltimore, MD, United States of America
                Article
                5597
                10.7717/peerj.5597
                6139016
                9ba4373d-0b4e-4473-8f9f-8af24fd6ce77
                ©2018 Myint et al.

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited.

                History
                : 17 May 2018
                : 16 August 2018
                Funding
                Funded by: National Institutes of Health
                Award ID: R01GM115440
                This work was supported by a National Institutes of Health grant (R01GM115440). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Psychiatry and Psychology
                Science and Medical Education
                Statistics

                inference,behavior,causality,randomized trial,interpretation,explanation

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