Intuitive Biostatistics A Nonmathematical Guide to Statistical Thinking

by Motulsky, Harvey

• ISBN13: 9780199730063
• ISBN10: 0199730067
• Edition: 2nd
• Format: Paperback
• Copyright: 2010-01-20
• Publisher: Oxford University Press

Summary

Thoroughly revised and updated, the second edition of Intuitive Biostatistics retains and refines the core perspectives of the previous edition: a focus on how to interpret statistical results rather than on how to analyze data, minimal use of equations, and a detailed review of assumptions and common mistakes. Intuitive Biostatistics, Completely Revised Second Edition, provides a clear introduction to statistics for undergraduate and graduate students and also serves as a statistics refresher for working scientists. NEW TO THIS EDITION: * Chapter 1 shows how our intuitions lead us to misinterpret data, thus explaining the need for statistical rigor. * Chapter 11 explains the lognormal distribution, an essential topic omitted from many other statistics books. * Chapter 21 contrasts testing for equivalence with testing for differences. * Chapters 22, 23, and 40 explore the pervasive problem of multiple comparisons. * Chapters 24 and 25 review testing for normality and outliers. * Chapter 35 shows how statistical hypothesis testing can be understood as comparing the fits of alternative models. * Chapters 37 and 38 provide a brief introduction to multiple, logistic, and proportional hazards regression. * Chapter 46 reviews one example in great depth, reviewing numerous statistical concepts and identifying common mistakes. * Chapter 47 includes 49 multi-part problems, with answers fully discussed in Chapter 48. * New "Q and A" sections throughout the book review key concepts.

Author Biography

Previously held faculty position in the Department of Pharmacology at the University of California San Diego. Currently, founder of GraphPad Software, Inc.

Table of Contents

Preface

Introducing Statistics

Statistics and Probability Are Not Intuitive

Why Statistics Can Be Hard to Learn

From Sample to Population

Confidence Intervals

Confidence Interval of a Proportion

Confidence Interval of Survival Data

Confidence Interval of Counted Data

Continuous Variables

Graphing Continuous Data

Types of Variables

Quantifying Scatter

The Gaussian Distribution

The Lognormal Distribution and Geometric Mean

Confidence Interval of a Mean

The Theory of Confidence Intervals

Error Bars

P Values And Significance

Introducing P Values

Statistical Significance and Hypothesis Testing

Relationship Between Confidence Intervals and Statistical Significance

Interpreting a Result That is Statistically Significant

Interpreting a Result That Is Not Statistically Significant

Statistical Power

Testing For Equivalence or Noninferiority

Challenges In Statistics

Multiple Comparisons Concepts

Multiple Comparison Traps

Gaussian or Not?

Outliers

Statistical Tests

Comparing Observed and Expected Distributions

Comparing Proportions: Prospective and Experimental Studies

Comparing Proportions: Case-Control Studies

Comparing Survival Curves

Comparing Two Means: Unpaired t test

Comparing Two Paired Groups

Correlation

Fitting Models To Data

Simple Linear Regression

Models

Comparing Models

Nonlinear Regression

Multiple, Logistic, and Proportional Hazards Regression

Multiple Regression Traps

The Rest Of Statistics

Analysis of Variance

Multiple Comparison Tests After ANOVA

Nonparametric Methods

Sensitivity Specificity and Receiver-Operator Characteristic Curves

Sample Size

Putting It All Together

Statistical Advice

Choosing a Statistical Test

Capstone Example

Review Problems

Answers to Review Problems

AppendiceS

Statistics with GraphPad

Statistics With Excel

Statistics R

Values of the t Distribution Needed to Compute CIs

A Review of Logarithms

References

Table of Contents provided by Publisher. All Rights Reserved.