## Common Errors in Statistics (and How to Avoid Them)Praise for the First Edition of Common Errors in Statistics " . . . let me recommend Common Errors to all those who interact with statistics, whatever their level of statistical understanding . . . " --Stats 40 " . . . written . . . for the people who define good practice rather than seek to emulate it." --Journal of Biopharmaceutical Statistics " . . . highly informative, enjoyable to read, and of potential use to a broad audience. It is a book that should be on the reference shelf of many statisticians and researchers." --The American Statistician " . . . I found this book the most easily readable statistics book ever. The credit for this certainly goes to Phillip Good." --E-STREAMS A tried-and-true guide to the proper application of statistics Now in a second edition, the highly readable Common Errors in Statistics (and How to Avoid Them) lays a mathematically rigorous and readily accessible foundation for understanding statistical procedures, problems, and solutions. This handy field guide analyzes common mistakes, debunks popular myths, and helps readers to choose the best and most effective statistical technique for each of their tasks. Written for both the newly minted academic and the professional who uses statistics in their work, the book covers creating a research plan, formulating a hypothesis, specifying sample size, checking assumptions, interpreting p-values and confidence intervals, building a model, data mining, Bayes' Theorem, the bootstrap, and many other topics. The Second Edition has been extensively revised to include: * Additional charts and graphs * Two new chapters, Interpreting Reports and Which Regression Method? * New sections on practical versus statistical significance and nonuniqueness in multivariate regression * Added material from the authors' online courses at statistics.com * New material on unbalanced designs, report interpretation, and alternative modeling methods With a final emphasis on both finding solutions and the great value of statistics when applied in the proper context, this book is eminently useful to students and professionals in the fields of research, industry, medicine, and government. |

### Contents

PART II HYPOTHESIS TESTING AND ESTIMATION | 45 |

PART III BUILDING A MODEL | 145 |

Appendix A | 195 |

Appendix B | 205 |

Glossary Grouped by Related but Distinct Terms | 219 |

Bibliography | 223 |

Author Index | 243 |

Subject Index | 249 |

### Other editions - View all

Common Errors in Statistics (and How to Avoid Them) Phillip I. Good,James W. Hardin No preview available - 2006 |

Common Errors in Statistics (and How to Avoid Them) Phillip I. Good,James W. Hardin No preview available - 2006 |

### Common terms and phrases

2006 John Wiley alternative hypothesis Altman DG analysis apparent error apply associated assumptions Bayes factor Bayesian Berger bias bootstrap bootstrap sample Chapter clinical trials coefficients Common Errors confidence interval correct correlation cross-validation decision Deming regression denote depend difference equation Errors in Statistics example expected excess error experiment experimental design F-ratio Figure goals scored graphic Hardin independent interval estimate jackknife labels large number linear regression logistic regression loss function main effects mean measure median meta-analysis multivariate Node normal distribution null hypothesis number of goals observations obtain outcome p-value parameter patients permutation test plot population prediction rule predictor prior probability problem random regression model selection significance level simulations specific square statistical methods statistical procedures statistically significant statistician subgroup survey t-test tables Team test statistic tion Total Number treatment true Type II error validation values variance zero

### Popular passages

Page 82 - Behold! human beings living in an underground den, which has a mouth open towards the light and reaching all along the den; here they have been from their childhood, and have their legs and necks chained so that they cannot move, and can only see before them, being prevented by the chains from turning round their heads.

Page 82 - ... here they have been from their childhood, and have their legs and necks chained so that they cannot move, and can only see before them, being prevented by the chains from turning round their heads. Above and behind them a fire is blazing at a distance, and between the fire and the prisoners there is a raised way; and you will see, if you look, a low wall built along the way, like the screen which marionette players have in front of them, over which they show the puppets.

Page 83 - And suppose further that the prison had an echo which came from the other side, would they not be sure to fancy when one of the passers-by spoke that the voice which they heard came from the passing shadow? No question, he replied. To them, I said, the truth would be literally nothing but the shadows of the images.

Page 106 - The standard deviation divided by the square root of the sample size is called the standard error. The standard error of the mean is the standard deviation of the sampling distribution of the mean.

Page 223 - Altman DG, Lausen B, Sauerbrei W, Schumacher M. Dangers of using "optimal" cutpoints in the evaluation of prognostic factors.

Page 225 - Block G. A review of validations of dietary assessment methods. Am J Epidemiol 1982; 1 15:492-505.

Page 187 - The simple idea of splitting a sample into two and then developing the hypothesis on the basis of one part and testing it on the remainder may perhaps be said to be one of the most seriously neglected ideas in statistics, if we measure the degree of neglect by the ratio of the number of cases where a method could give help to the number of cases where it is actually used.

Page 223 - Altman DG, De Stavola BL, Love SB, Stepniewska KA. Review of survival analyses published in cancer journals. Br J Cancer 1995;72:51 1-518.

### References to this book

Computational and Mathematical Modeling in the Social Sciences Scott de Marchi No preview available - 2005 |