## Intuitive Biostatistics: A Nonmathematical Guide to Statistical Thinking"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"--Provided by publisher. |

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#### LibraryThing Review

User Review - miki - LibraryThingI often recommend this book to two different groups: * colleagues who want to have a better understanding of the factors that drive statistical methods in medical research, without having to learn the ... Read full review

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Seems to be a gud book , for understanding applying Statistical Tests. Must go through ........

### Contents

PREFACE | |

Introducing Statistics | |

Confidence Intervals | |

Continuous Variables | |

P Values and Significance | |

Challenges in Statistics | |

Statistical Tests | |

Fitting Models to Data | |

The Rest of Statistics | |

Putting It All Together | |

APPENDICES | |

### Other editions - View all

Intuitive Biostatistics: A Nonmathematical Guide to Statistical Thinking Harvey Motulsky Limited preview - 2013 |

### Common terms and phrases

analysis analyze answer assume assumption average best-fit value calculate case–control study cells censored chance Chapter computed conclusion correlation coefficient count data sets defined difference disease drug effect equal equation error bars example expect experiment experimental explained Figure fraction Gaussian distribution graph hypothesis is true Ifthe Ifyou independent variables interpret investigators larger linear regression logarithms logistic regression lognormal distribution measured method multiple comparison tests multiple comparisons multiple regression nonlinear regression nonparametric tests normality test null hypothesis number of subjects observed odds ratio ofthe one-tail P value one-way ANOVA outcome outliers paired parameters patients plots Poisson distribution population mean predicted probability quantify randomly range receptors relative risk sample mean scatter shows significance level simulated slope standard error statistical hypothesis testing statistically significant sum of squares survival curves Table tion treatment two-tail P value Type II error unpaired t test zero