Experimental Design and Statistical Analysis for Pharmacology and the Biomedical Sciences

Paul J. Mitchell 지음

Wiley-Blackwell 2022년 04월 04일 출간

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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 Scie

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Foreward 4 1 Introduction 6 2 So, what are data? 8 3 Numbers; counting and measuring, precision and accuracy 9 4 Data collection: Sampling and populations, different types of data, data distributions 12 5 Descriptive statistics: measures to describe and summarize data sets. 16 6 Testing for Normality and transforming skewed data sets 22 7 The Standard Normal Distribution 28 8 Non-Parametric Descriptive statistics 30 9 Summary of descriptive statistics; so, what values may I use to describe my data? 34 Decision Flowchart 1: Descriptive Statistics Parametric v Non-parametric data 43 10 Introduction to Inferential statistics 44 11 Comparing 2 sets of data Independent t-test 50 12 Comparing 2 sets of data Paired t-test 55 13 Comparing 2 sets of data Independent non-parametric data 58 14 Comparing 2 sets of data Paired non-parametric data 62 15 Parametric 1-way Analysis of Variance 66 16 Repeated Measures Analysis of Variance 78 17 Complex Analysis of Variance models 86 18 Non-parametric ANOVA 102 Decision Flowchart 2: Inferential Statistics Single and multiple pairwise comparisons 115 19 Correlation Analysis 116 20 Regression Analysis 126 21 Chi-Square Analysis 136 Decision Flowchart 3: Inferential Statistics Tests of Association 145 22 Confidence Intervals 146 23 Permutation Test of Exact Inference 150 24 General Linear Model 152 Appendices Introduction to Appendices 155 A Data distribution: probability mass function and probability density functions A.1 Binomial Distribution 156 A.2 Exponential Distribution 157 A.3 Normal Distribution 158 A.4 Chi-square Distribution 159 A.5 Student t-Distribution 160 A.6 F Distribution 161 B Standard Normal Probabilities B.1 AUC values for z values below the me

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저자(글) Paul J. Mitchell

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.