## Understanding BiostatisticsUnderstanding Biostatistics looks at the fundamentals of biostatistics, using elementary statistics to explore the nature of statistical tests. This book is intended to complement first-year statistics and biostatistics textbooks. The main focus here is on ideas, rather than on methodological details. Basic concepts are illustrated with representations from history, followed by technical discussions on what different statistical methods really mean. Graphics are used extensively throughout the book in order to introduce mathematical formulae in an accessible way.
- Discusses confidence intervals and p-values in terms of confidence functions.
- Explains basic statistical methodology represented in terms of graphics rather than mathematical formulae, whilst highlighting the mathematical basis of biostatistics.
- Looks at problems of estimating parameters in statistical models and looks at the similarities between different models.
- Provides an extensive discussion on the position of statistics within the medical scientific process.
- Discusses distribution functions, including the Guassian distribution and its importance in biostatistics.
This book will be useful for biostatisticians with little mathematical background as well as those who want to understand the connections in biostatistics and mathematical issues. |

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### Contents

Factors | |

Study design and the bias issue | |

The anatomy of a statistical test | |

Learning about parameters and some | |

Empirical distribution functions | |

Correlation and regression in bivariate | |

How to compare the outcome in two groups | |

### Common terms and phrases

actually analysis analyze ANCOVA approximation assume assumption baseline Bayesian bias binomial distribution bivariate Gaussian distribution called cancer chapter clinical trials coefficient compute confidence function confidence interval confidence limits confounders correlation corresponding covariates dashed curve defined denote density derive described discussion disease dose dose-response drug e-CDF error estimating equation event example experiment exponential family exposure Figure frequentist given graph hazard heterogeneity Hodgkin's lymphoma hypergeometric distribution illustrated important independent individual Kaplan-Meier estimate large-sample linear logistic regression Mantel-Haenszel mathematical matrix maximum likelihood mean difference mean values measure median method notation null hypothesis observation obtained odds ratio outcome variable p-value particular patients percentiles placebo population predictive probability problem proportional quadratic form random relation result sample Section significance level standard stochastic variable t-test test statistic theorem treatment effect true univariate variance vector whereas Wilcoxon test zero