Pub. date: 2007 | Online Pub. Date: September 15, 2007 | DOI: 10.4135/9781412952651 | Print ISBN: 9781412924702 | Online ISBN: 9781412952651| Publisher:SAGE Publications, Inc.About this encyclopedia
Allen I. Huffcutt
The best way to understand meta-analysis is to begin with a review of basic statistics. There are two main areas in statistics: descriptive and inferential. The former deals with the basic organization and presentation of data, the latter with the process of deriving conclusions and generalizations (i.e., inferences) about a population based on an analysis of sample data taken from that population. Significance testing is an older and more traditional means of making inferences about populations based on sample data. Developed by the eminent statistician Ronald Fisher during the early 1930s, significance testing focuses on the concept of the null hypothesis and involves estimating the probability that differences observed in a sample occurred entirely by chance, with no true effect in the corresponding population. The real strength of significance testing is that it constrains Type I errors (i.e., rejecting the null hypothesis when there is no true effect in the ...