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(Circulation. 2006;114:76-81.)
© 2006 American Heart Association, Inc.

Statistical Primer for Cardiovascular Research

Descriptive Statistics and Graphical Displays

Martin G. Larson, SD

From the Department of Mathematics and Statistics, Boston University, Boston, and the National Heart, Lung, and Blood Institute’s Framingham Heart Study, Framingham, Mass.

Correspondence to Martin Larson, SD, Framingham Heart Study, 73 Mount Wayte Ave, Framingham, MA 01702. E-mail mlarson@bu.edu

Key Words: epidemiology • information display • normal distribution • statistics

An extract of the first 250 words of the full text is provided, because this article has no abstract.

	Introduction

 
Statistics is a broad mathematical discipline dealing with techniques for the collection, analysis, interpretation, and presentation of numerical data. Data are information used for reasoning, discussion, or calculation; data are the foundation of modern scientific inference. Data may be obtained by a formal sampling procedure, by recording responses to experimental conditions, or by observing a process repeatedly over time. Once data are collected, statistical analysis typically begins by calculating descriptive statistics—numbers that characterize features of those specific data—and by presenting the descriptive statistics in tables or graphs. In contrast, inferential statistics—statistics for making inferences about the populations from which data are sampled—is a related, broader category of statistical analysis. Forthcoming articles in this series will cover topics in inferential statistics. In this article, we consider elementary descriptive statistics. Similar material can be found in standard statistical textbooks or online.^1–4

	Types of Variables

 
The characteristics of interest in a research study are called variables, measurable quantities that vary among individuals (for our purposes, an individual may be a person, animal, place, or thing) or within individuals over time. By contrast, parameters are not actual measurements or attributes of individuals but are quantities that define a statistical model.

Variables are classified as discrete or continuous: Discrete variables can assume only certain values (fixed and readily countable), whereas continuous variables can assume an infinite number of values. Examples of discrete variables commonly encountered in cardiovascular research include species, strain, racial/ethnic group, sex, education level, treatment group, hypertension status, and New York Heart . . . [Full Text of this Article]

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