jroese@lssu.edu GLOSSARY As with any new topic, knowledge of the language of statistics is a prerequisite to understanding the concepts.  Many of the terms used by statisticians are commonly used words with rather imprecise definitions (e.g. population, sample, mean).  However, in a statistical context these words take on very specific meanings.  The proper use if these terms is an essential element in communicating statistical information. Confidence interval A range of values that, with a measurable degree of certainty, contain a population parameter. Continuous Interval data in which observations can assume an infinite number of values (i.e. decimal values). Data The set of observations of a population or sample. Degrees of freedom A measure of the amount of information available from a sample or samples. Discrete Any scale of measurement in which observations are restricted to a finite set of mutually exclusive categories or integer values. Homoscedastic A term referring to the similarity of variances between two or more populations of interest. Independent A term used to indicate the lack of influence  the selection of one observation has on the selection of other observations. Interval A scale of measurement in which observations are measured on a proportional basis (e.g. number of individuals, weight, length). Mean A measure of central tendency among the observations of a population or sample; the arithmetic average of the data. Median A measure of central tendency among the observations of a population or sample; the middle value in an ordered data set. Mode A measure of central tendency among the observations of a population or sample; the most frequently occurring value(s) in the data. Nominal A scale of measurement in which observations are classified into mutually exclusive categories which lack any ranking or proportional relationship (e.g. male and female; herbivore, carnivore, omnivore). Observation The value of a variable for an individual sampling unit. Ordinal A scale of measurement in which observations are classified into mutually exclusive categories which can be ranked, but which lack a proportional relationship (e.g. slow, and fast; small, medium, large). Parameter A quantity that summarizes or characterizes a population. Parametric Refers to statistical procedures that depend on the estimation of parameters. Population All objects of a similar type, or some designated subset of these objects (e.g. all white-tailed deer in Michigan). Sample A subset of a population, usually understood to be randomly selected. Sampling Unit The objects which comprise a population or sample.  These are the subject upon which measurements are made (e.g. a deer). Standard deviation A measure of dispersion of the observations around the population or sample mean; the square root of the variance. Standard error A measure of dispersion of a group of sample parameters around a population (e.g. standard error of the mean). Statistic A quantity that summarizes or characterizes a sample. Variable A measurable characteristic of the sampling units that may assume any one of a set of values (e.g. the weight of each deer). Variance A measure of dispersion of the observations around the population or sample mean; the sum of the squared differences between each observation and the mean. Variation Differences, naturally occurring or designed, between observations.