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      Is Most Published Research Really False?

      1 , 2 , 1
      Annual Review of Statistics and Its Application
      Annual Reviews

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          Abstract

          There has been an increasing concern in both the scientific and lay communities that most published medical findings are false. But what does it mean to be false? Here we describe the range of definitions of false discoveries in the scientific literature. We summarize the philosophical, statistical, and experimental evidence for each type of false discovery. We discuss common underpinning problems with the scientific and data analytic practices and point to tools and behaviors that can be implemented to reduce the problems with published scientific results.

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          Most cited references27

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          Is Open Access

          Galaxy: a comprehensive approach for supporting accessible, reproducible, and transparent computational research in the life sciences

          Increased reliance on computational approaches in the life sciences has revealed grave concerns about how accessible and reproducible computation-reliant results truly are. Galaxy http://usegalaxy.org, an open web-based platform for genomic research, addresses these problems. Galaxy automatically tracks and manages data provenance and provides support for capturing the context and intent of computational methods. Galaxy Pages are interactive, web-based documents that provide users with a medium to communicate a complete computational analysis.
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            The ASA's Statement onp-Values: Context, Process, and Purpose

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              Reproducible research in computational science.

              Roger Peng (2011)
              Computational science has led to exciting new developments, but the nature of the work has exposed limitations in our ability to evaluate published findings. Reproducibility has the potential to serve as a minimum standard for judging scientific claims when full independent replication of a study is not possible.
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                Author and article information

                Journal
                Annual Review of Statistics and Its Application
                Annu. Rev. Stat. Appl.
                Annual Reviews
                2326-8298
                2326-831X
                March 07 2017
                March 07 2017
                : 4
                : 1
                : 109-122
                Affiliations
                [1 ]Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland 21205;
                [2 ]Center for Computational Biology, Johns Hopkins University, Baltimore, Maryland 21205
                Article
                10.1146/annurev-statistics-060116-054104
                7a58c07c-a056-4f6a-a8bc-a5d0f20742ac
                © 2017
                History

                Sociology,Social policy & Welfare,Political science,Psychology,Development studies,Public health

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