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Ritchie Stuart. Science Fictions: How Fraud, Bias, Negligence, and Hype Undermine the Search for Truth

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Ritchie Stuart. Science Fictions: How Fraud, Bias, Negligence, and Hype Undermine the Search for Truth
Metropolitan Books, 2020. — 368 p. — eISBN 9781250222688.
Hypeology will expose the bias, hype, incompetence and fraud that plague the peer-reviewed world where many of the most seductive and striking scientific studies originate, and will take a Freakonomics-style look at the implications of this crisis for us all.
A whole industry of books and TED-talks has been built on the findings of psychological studies, presenting them as a toolkit we can apply to our lives. That industry proved resilient in the face of the ‘Replication Crisis’ of 2011 that revealed hundreds of famous experiments to be unrepeatable, dramatically undermining their conclusions. But as Dr Stuart Ritchie will explain, far from being solved that crisis has since spread to other disciplines.
In one large-scale organised replication effort in 2015, in which psychologists repeated 100 experiments from top academic journals, only 39 of those papers’ results were successfully replicated. Another recent analysis found that almost half of 30,000 scientific papers published in peer-reviewed journals contained numerical errors; 15% of them contained errors so severe that they entirely flipped the results described in the paper. A 2016 survey of scientists across all fields found that 52% believed science is facing a “significant crisis”.
Hypeology will explain how bad practice and dodgy results have become endemic in contemporary science, and it will provide a devastating and gripping take-down of the over-simplified way many of us – including scientists – present and interpret even those results that are reliable. It will be a celebration of everything that is great about science, a manifesto for a better and more rigorous way of thinking about scientific data and its importance for society, and it will provide the reader with a toolkit for spotting bad science for themselves in order to save science from itself.
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