Download Ebook Experimental Design for Laboratory Biologists: Maximising Information and Improving Reproducibility
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Experimental Design for Laboratory Biologists: Maximising Information and Improving Reproducibility
Download Ebook Experimental Design for Laboratory Biologists: Maximising Information and Improving Reproducibility
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Review
'This is a wonderfully lucid introduction to experimental design, written by an author who is clearly aware of the pitfalls that exist for the unwary experimenter. The focus is on how to design experiments to ensure reproducible research, with many examples illustrating general principles that need to be understood to avoid error and bias. The coverage of statistical analysis follows on naturally from the design issues, and is amply illustrated with exercises in R. Highly recommended.' Dorothy Bishop, University of Oxford'Worldwide there is a salient discussion about deficiencies in the validity and predictiveness of research in the life sciences. Indeed, a fullblown 'reproducibility crisis' has been proclaimed. Against this backdrop this important textbook is a timely and highly useful contribution in the pressing quest to improve the robustness, rigor, and reproducibility of current biological and preclinical research. Proper experimental design is the bedrock for obtaining reliable evidence. By providing the necessary conceptual know-how and practical knowledge, [this book] enables investigators in all stages of their careers to minimize bias and improve statistical power through proper design and analysis of their experiments. This volume is unique ... [as] it is immensely readable and accessible even for those with little previous knowledge, in combining all relevant aspects in a practical, concise and comprehensive manner, and in its clear focus on factors that help to improve the quality of research.' Ulrich Dirnagl, Charité University Hospital, Germany'There is an increasing need to better design experiments not only to reduce the number of any animals being used in any such work, but also to ensure that the data so produced is meaningful. As part of that process knowing how to power studies and then properly analyse the data so generated is vital, and of late there have been concerns that this is not been done to same vigour as that seen in the clinical arena. However, most scientists struggle with this aspect of their work, and thus it is really refreshing to come across a book that explicitly deals with experimental design and analysis. This new book clearly lays out what can and should be done and is written by an acknowledged expert and I have no doubt that this book will become a recommended read for all those contemplating undertaking work of this type.' Roger Barker, University of Cambridge
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Book Description
An ideal resource for anyone conducting lab-based biomedical research, this guide shows how to design reproducible experiments that have low bias, high precision and widely applicable results. It explores key ideas in experimental design, including reproducibility and replication, assesses common designs, and shows how to plan for success.
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Product details
Paperback: 422 pages
Publisher: Cambridge University Press; 1 edition (January 16, 2017)
Language: English
ISBN-10: 9781107424883
ISBN-13: 978-1107424883
ASIN: 1107424887
Product Dimensions:
7.5 x 0.8 x 9.6 inches
Shipping Weight: 2 pounds (View shipping rates and policies)
Average Customer Review:
4.5 out of 5 stars
2 customer reviews
Amazon Best Sellers Rank:
#697,354 in Books (See Top 100 in Books)
This was an exceptional primer on experimental design for laboratory biologists. I've conducted wet-lab research, and have greatly benefited from this practical text. The author discusses inherent challenges in conducting experimental research along with approaches to mitigate these challenges in a clear and concise manner. Additionally, he provides great tutorials on doing analysis using R. Highly recommend.
It was a pretty good book about experimental design, at times I felt it was too nitty gritty without giving you the big picture at the same time. So really good for direct application but less useful for mastering the fundamental principles of research flaws.
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