Experimental Design and Statistical Analysis for Pharmacology and the Biomedical Sciences
eBook - PDF

Experimental Design and Statistical Analysis for Pharmacology and the Biomedical Sciences

  1. English
  2. PDF
  3. Available on iOS & Android
eBook - PDF

Experimental Design and Statistical Analysis for Pharmacology and the Biomedical Sciences

About this book

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 analyze 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 analyze 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.

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Yes, you can access Experimental Design and Statistical Analysis for Pharmacology and the Biomedical Sciences by Paul J. Mitchell in PDF and/or ePUB format, as well as other popular books in Medicine & Epidemiology. We have over one million books available in our catalogue for you to explore.

Information

Publisher
Wiley-Blackwell
Year
2022
Print ISBN
9781119437635
eBook ISBN
9781119437673
Edition
1
Topic
Medicine
Subtopic
Epidemiology

Table of contents

  1. Cover
  2. Title Page
  3. Copyright Page
  4. Biography
  5. Contents
  6. Acknowledgements
  7. Foreword
  8. Chapter 1 Introduction
  9. Chapter 2 So, what are data?
  10. Chapter 3 Numbers; counting and measuring, precision, and accuracy
  11. Chapter 4 Data collection: sampling and populations, different types of data, data distributions
  12. Chapter 5 Descriptive statistics; measures to describe and summarise data sets
  13. Chapter 6 Testing for normality and transforming skewed data sets
  14. Chapter 7 The Standard Normal Distribution
  15. Chapter 8 Non-parametric descriptive statistics
  16. Chapter 9 Summary of descriptive statistics: so, what values may I use to describe my data?
  17. Chapter 10 Introduction to inferential statistics
  18. Chapter 11 Comparing two sets of data – Independent t-test
  19. Chapter 12 Comparing two sets of data – Paired t-test
  20. Chapter 13 Comparing two sets of data – independent non-parametric data
  21. Chapter 14 Comparing two sets of data – paired non-parametric data
  22. Chapter 15 Parametric one-way analysis of variance
  23. Chapter 16 Repeated measure analysis of variance
  24. Chapter 17 Complex Analysis of Variance Models
  25. Chapter 18 Non-parametric ANOVA
  26. Chapter 19 Correlation analysis
  27. Chapter 20 Regression analysis
  28. Chapter 21 Chi-square analysis
  29. Chapter 22 Confidence intervals
  30. Chapter 23 Permutation test of exact inference
  31. Chapter 24 General Linear Model
  32. Appendix A: Data distribution: probability mass function and probability density functions
  33. Appendix B: Standard normal probabilities
  34. Appendix C: Critical values of the t-distribution
  35. Appendix D: Critical values of the Mann–Whitney U-statistic
  36. Appendix E: Critical values of the F distribution
  37. Appendix F: Critical values of chi-square distribution
  38. Appendix G: Critical z values for multiple non-parametric pairwise comparisons
  39. Appendix H: Critical values of correlation coefficients
  40. Index
  41. EULA