## Babylon English

technique or procedure of synthesizing the results of a research by combining the results of a number of independent studies

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technique or procedure of synthesizing the results of a research by combining the results of a number of independent studies

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Glossary of Terms

Process of using statistical methods to combine the results of different studies. A frequent application is pooling the results from a set of randomized controlled trials, none of which alone is powerful enough to demonstrate statistical significance.

National Institutes of HealthThe Glossary of the American Council on Science and Health

A method for statistically combining the findings of independent studies (usually published studies and usually many) into a group of conclusions.

Social Work in Canada

A statistical technique to combine information from many empirical studies on a topic to objectively estimate the reliability and overall size of the effect.

English Wikipedia - The Free Encyclopedia

The basic tenet of a **meta-analysis** is that there is a common truth behind all conceptually similar scientific studies, but which has been measured with a certain error within individual studies. The aim in meta-analysis then is to use approaches from statistics to derive a pooled estimate closest to the unknown common truth based on how this error is perceived. In essence, all existing methods yield a weighted average from the results of the individual studies and what differs is the manner in which these weights are allocated and also the manner in which the uncertainty is computed around the point estimate thus generated. In addition to providing an estimate of the unknown common truth, meta-analysis has the capacity to contrast results from different studies and identify patterns among study results, sources of disagreement among those results, or other interesting relationships that may come to light in the context of multiple studies. Meta-analysis can be thought of as "conducting research about previous research." Meta-analysis can only proceed if we are able to identify a common statistical measure that is shared among studies, called the effect size, which has a standard error so that we can proceed with computing a weighted average of that common measure. Such weighting usually takes into consideration the sample sizes of the individual studies, although it can also include other factors, such as study quality.

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