Statistical Terms You Should Know

From Ashley Crossman, former About.com Guide

31. Recursive Relationship

In statistics, a recursive relationship between two variables is one in which the causal effects run in only one direction.

32. Regression Analysis

Regression is a statistical technique that is used to learn more about the relationship between an independent (predictor) variable and a dependent (criterion) variable.

33. Sampling Distribution

In statistics, a sampling distribution is a theoretical mathematical distribution of all the possible sample outcomes that can be obtained by selecting samples from a population.

34. Sampling Weight

A sampling weight is a statistical correction factor that compensates for a sample design that tends to over- or under-represent various segments within a population.

35. Secondary Analysis

Secondary analysis is the practice of analyzing data that have already been gathered by someone else, often for a distinctly different purpose.

36. Specification

In statistics, specification is the practice of seeing if a particular relationship among variables remains the same in different segments of a population.

37. Spuriousness

In statistics, spuriousness occurs when two variables that are statistically related to each other, but have no causal relationship.

38. Standard Error

A standard error is a statistical measure of the amount of variation in a sampling distribution.

39. Standardization

Standardization is a statistical technique that gives different units of measurement a common base for purposes of comparison.

40. Supressor Variable

In statistics, a suppressor variable is a variable that, when controlled, has the effect of strengthening the relationship between two other variables.

41. Variance

Variance is a statistical measure of the degree to which scores in a distribution differ from one another, especially in relation to the mean of the distribution.

42. Z-Score

In statistics, a z-score indicates how many standard deviations an observation is above or below the mean.