The best way to understand meta-analysis is to begin with a review of basic statistics. There are two main areas: descriptive and inferential. The former deals with the organization and presentation of data, the latter with the process of deriving conclusions and generalizations about a population on the basis of an analysis of sample data taken from it. The root word for inferential is infer, common definitions of which include terms such as deduce, surmise, and even best guess. These terms all capture the fundamental notion that the generalizations made when going from a sample to a population tend to be imperfect, even to the point of being totally wrong.

Significance testing is the older and more traditional means of making population inferences. Developed by the ...

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