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        Understanding Statistics and Experimental Design

        How to Not Lie with Statistics

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        Author(s)
        Herzog, Michael H.
        Francis, Gregory
        Clarke, Aaron
        Language
        English
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        Abstract
        This open access textbook provides the background needed to correctly use, interpret and understand statistics and statistical data in diverse settings. Part I makes key concepts in statistics readily clear. Parts I and II give an overview of the most common tests (t-test, ANOVA, correlations) and work out their statistical principles. Part III provides insight into meta-statistics (statistics of statistics) and demonstrates why experiments often do not replicate. Finally, the textbook shows how complex statistics can be avoided by using clever experimental design. Both non-scientists and students in Biology, Biomedicine and Engineering will benefit from the book by learning the statistical basis of scientific claims and by discovering ways to evaluate the quality of scientific reports in academic journals and news outlets.
        URI
        http://library.oapen.org/handle/20.500.12657/23029
        Keywords
        Medicine; Molecular biology; Biostatistics; Science education; Statistics; Experiential research; Behavioral sciences; Textbook
        DOI
        10.1007/978-3-030-03499-3
        Publisher
        Springer Nature
        Publisher website
        https://www.springernature.com/gp/products/books
        Publication date and place
        Cham, 2019
        Series
        Learning Materials in Biosciences,
        Classification
        Psychological methodology
        Teaching of a specific subject
        Medical research
        Probability and statistics
        Life sciences: general issues
        Ethology and animal behaviour
        Pages
        142
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        License

        • If not noted otherwise all contents are available under Attribution 4.0 International (CC BY 4.0)

        Credits

        • logo EU
        • This project received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 683680, 810640, 871069 and 964352.

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