## Abstract

This chapter gives an overview of biostatistics ideas. Biostatistics is the discipline concerned with the design and analysis of data from biomedical studies. It comprises a set of principles and methods for generating and using quantitative evidence to address scientific questions, for estimating unknown quantities, and for quantifying the uncertainty in estimates. Biostatistics is the search for truth (or at least true characteristics of populations). The two major tools of statistical inference are estimation and hypothesis testing. The study first introduces one of the seminal discoveries in the history of mathematics and science: the central limit theorem. Biostatistics comprises ideas and methods for quantifying the evidence in data to distinguish among competing hypotheses, for estimating unknown characteristics of populations, and for quantifying the uncertainty in those estimates. The essential idea is to use a statistic for a representative sample to estimate an unknown population parameter. A biostatistics mantra about data is: display, look, and think. Effective displays show the patterns of scientific import but also must provoke new questions about individual or subgroups of observations. When looking at data, one must be cognizant that it is human nature to overinterpret small data sets, that is, see patterns where none actually exists. The chapter concludes by explaining regression analysis. © 2009

Original language | English (US) |
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Title of host publication | Clinical and Translational Science |

Publisher | Elsevier Inc. |

Pages | 59-68 |

Number of pages | 10 |

ISBN (Print) | 9780123736390 |

DOIs | |

State | Published - Jan 1 2009 |

## ASJC Scopus subject areas

- Neuroscience(all)
- Biochemistry, Genetics and Molecular Biology(all)
- Pharmacology, Toxicology and Pharmaceutics(all)