Description

Meta Analysis: A Guide to Calibrating and Combining Statistical Evidence acts as a source of basic methods for scientists wanting to combine evidence from different experiments. The authors aim to promote a deeper understanding of the notion of statistical evidence.

The book is comprised of two parts – The Handbook, and The Theory. The Handbook is a guide for combining and interpreting experimental evidence to solve standard statistical problems. This section allows someone with a rudimentary knowledge in general statistics to apply the methods. The Theory provides the motivation, theory and results of simulation experiments to justify the methodology.

This is a coherent introduction to the statistical concepts required to understand the authors’ thesis that evidence in a test statistic can often be calibrated when transformed to the right scale.

Meta Analysis: A Guide to Calibrating and Combining Statistical Evidence

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Paperback / softback by Elena Kulinskaya , Stephan Morgenthaler

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Meta Analysis: A Guide to Calibrating and Combining Statistical Evidence acts as a source of basic methods for scientists wanting... Read more

    Publisher: John Wiley & Sons Inc
    Publication Date: 29/02/2008
    ISBN13: 9780470028643, 978-0470028643
    ISBN10: 0470028645

    Number of Pages: 282

    Non Fiction , Mathematics & Science , Education

    Description

    Meta Analysis: A Guide to Calibrating and Combining Statistical Evidence acts as a source of basic methods for scientists wanting to combine evidence from different experiments. The authors aim to promote a deeper understanding of the notion of statistical evidence.

    The book is comprised of two parts – The Handbook, and The Theory. The Handbook is a guide for combining and interpreting experimental evidence to solve standard statistical problems. This section allows someone with a rudimentary knowledge in general statistics to apply the methods. The Theory provides the motivation, theory and results of simulation experiments to justify the methodology.

    This is a coherent introduction to the statistical concepts required to understand the authors’ thesis that evidence in a test statistic can often be calibrated when transformed to the right scale.

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