Experimental Design and Statistical Analysis for Pharmacology and the Biomedical Sciences

ISBN-13: 9781119437635 / Angielski / Miękka / 2022 / 256 str.

UK; National University of Irel Paul J. (University of Bath

Opis

Experimental Design and Statistical Analysis for Pharmacologists will provide an invaluable teaching resource for students, lecturers and researchers in pharmacology. The book will guide the reader through the basic principles of experimental design and statistical analysis, data presentation, descriptive statistics and inferential statistics (including power analysis, analysis of 2 groups of data, analysis of more than 2 groups of data (Analysis of Variance, post hoc and a priori analysis), the relationship between 2 variables, confidence intervals and General Linear Modelling). A large array of examples will be used throughout the book, describing the variety of experimental methods for which the statistical tests are appropriate; such experimental methods will include examples from molecular and cellular pharmacology, in vitro pharmacology, including isolated tissue techniques, and in vivo pharmacology. In addition, it is envisaged that each section will link directly to a dynamic and organic database that will provide further examples of data analysis coupled with descriptions of appropriate experimental design. This approach ties in with the eLearning platform proposed by the British Pharmacological Society (BPS) in support of the Core Curriculum for Pharmacology.

Spis treści

Foreward 41 Introduction 62 So, what are data? 83 Numbers; counting and measuring, precision and accuracy 94 Data collection: Sampling and populations, different types of data, data distributions 125 Descriptive statistics: measures to describe and summarize data sets. 166 Testing for Normality and transforming skewed data sets 227 The Standard Normal Distribution 288 Non-Parametric Descriptive statistics 309 Summary of descriptive statistics; so, what values may I use to describe my data? 34Decision Flowchart 1: Descriptive Statistics - Parametric v Non-parametric data 4310 Introduction to Inferential statistics 4411 Comparing 2 sets of data - Independent t-test 5012 Comparing 2 sets of data - Paired t-test 5513 Comparing 2 sets of data - Independent non-parametric data 5814 Comparing 2 sets of data - Paired non-parametric data 6215 Parametric 1-way Analysis of Variance 6616 Repeated Measures Analysis of Variance 7817 Complex Analysis of Variance models 8618 Non-parametric ANOVA 102Decision Flowchart 2: Inferential Statistics - Single and multiple pairwise comparisons 11519 Correlation Analysis 11620 Regression Analysis 12621 Chi-Square Analysis 136Decision Flowchart 3: Inferential Statistics -Tests of Association 14522 Confidence Intervals 14623 Permutation Test of Exact Inference 15024 General Linear Model 152Appendices Introduction to Appendices 155A Data distribution: probability mass function and probability density functionsA.1 Binomial Distribution 156A.2 Exponential Distribution 157A.3 Normal Distribution 158A.4 Chi-square Distribution 159A.5 Student t-Distribution 160A.6 F Distribution 161B Standard Normal ProbabilitiesB.1 AUC values for z values below the mean (i.e. -z) 162B.2 AUC values for z values above the mean (i.e. +z) 163C Critical values of the t-distribution 164D Critical values of the Mann-Whitney U statisticD.1 Critical values for U; One-tailed test, p = 0.05 165D.2 Critical values for U; One-tailed test, p = 0.01 166D.3 Critical values for U; Two-tailed test, p = 0.05 167D.4 Critical values for U; Two-tailed test, p = 0.01 168E Critical values of the F distributionE.1 Critical values of F, p = 0.05 169E.2 Critical values of F, p = 0.01 170E.3 Critical values of F, p = 0.001 171F Critical values of the Chi-square distribution 172G Critical z values for multiple non-parametric pairwise comparisonsG.1 Critical values of z according to the number of comparisons 173G.2 Alternative critical values of z according to the number of comparisons when all groups have an equal number of subjects 173H Critical values of correlation coefficientsH.1 Pearson Product Moment Correlation 174H.2 Spearman Rank Correlation 174H.3 Kendall's Rank Correlation (Kendall's tau) 175Overall Decision Flowchart: Descriptive and Inferential Statistics 176Index

O autorze

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.