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Experimental Design and Statistical Analysis for Pharmacology and the Biomedical Sciences

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Experimental Design and Statistical Analysis for Pharmacology and the Biomedical Sciences by Paul J. Mitchell guides readers through the principles of designing experiments and analysing data in the fields of pharmacology and biomedical sciences. The book focuses on practical applications and is designed to help scientists develop robust research methodologies and interpret statistical results accurately. It serves as an educational tool, enhancing the reader's ability to conduct studies with a reliable framework.
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Format: Paperback / softback
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You might enjoy this book if you are interested in gaining a solid understanding of experimental design and statistical analysis, particularly in the fields of pharmacology and biomedical sciences. This insightful guide is perfect for those wanting to enhance their research skills and analytical capabilities within these scientific areas.

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Book Hero Magic formatted this description to make it easier to read. While it's new and still learning, it may not be perfect - your feedback is welcome! Description

Experimental Design and Statistical Analysis for Pharmacology and the Biomedical Sciences

A practical guide to the use of basic principles of experimental design and statistical analysis in pharmacology

Experimental Design and Statistical Analysis for Pharmacology and the Biomedical Sciences provides clear instructions on applying statistical analysis techniques to pharmacological data. Written by an experimental pharmacologist with decades of experience teaching statistics and designing preclinical experiments, this reader-friendly volume explains the variety of statistical tests that researchers require to analyse data and draw correct conclusions.

Detailed, yet accessible, chapters explain how to determine the appropriate statistical tool for a particular type of data, run the statistical test, and analyse and interpret the results. By first introducing basic principles of experimental design and statistical analysis, the author then guides readers through descriptive and inferential statistics, analysis of variance, correlation and regression analysis, general linear modelling, and more. Lastly, throughout the textbook are numerous examples from molecular, cellular, in vitro, and in vivo pharmacology which highlight the importance of rigorous statistical analysis in real-world pharmacological and biomedical research.

This textbook also:

  • Describes the rigorous statistical approach needed for publication in scientific journals
  • Covers a wide range of statistical concepts and methods, such as standard normal distribution, data confidence intervals, and post hoc and a priori analysis
  • Discusses practical aspects of data collection, identification, and presentation
  • Features images of the output from common statistical packages, including GraphPad Prism, Invivo Stat, MiniTab, and SPSS

Experimental Design and Statistical Analysis for Pharmacology and the Biomedical Sciences is an invaluable reference and guide for undergraduate and graduate students, post-doctoral researchers, and lecturers in pharmacology and allied subjects in the life sciences.

Book Details

INFORMATION

ISBN: 9781119437635

Publisher: John Wiley and Sons Ltd

Format: Paperback / softback

Date Published: 05 May 2022

Country: United Kingdom

Imprint: Wiley-Blackwell

Audience: Tertiary education

DIMENSIONS

Spine width: 15.0mm

Width: 213.0mm

Height: 274.0mm

Weight: 680g

Pages: 256

About the Author

Dr Paul J. Mitchell is Senior Lecturer and Associate Professor in the Department of Pharmacy and Pharmacology, University of Bath, UK, and Adjunct Lecturer in the Department of Pharmacology and Therapeutics, National University of Ireland (NUI), Galway, Ireland. He has more than 45 years’ experience in experimental pharmacology, experimental design, and statistical analysis. For the last 25 years Dr Mitchell has collaborated with colleagues to develop a coherent strategy to teach experimental design and statistical analysis to undergraduate and graduate students across subject areas.

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