Imagine you're at the FIFA Women’s World Cup finals and you learn that the average IQ of everyone in the stadium is 102.

Now, let's say you decide to estimate this average IQ for yourself. You randomly select 15 batches of 500 people from the stadium, asking each person to take an IQ test.

Next, you treat these 15 groups like 15 different studies and analyze them together. This is called a meta-analysis.

Specifically, a fixed-effect meta-analysis would be valid to use here because all effects are estimating the same underlying true average IQ, which is 102. Even though the average IQ in each group might not be exactly 102, you assume they all should average to 102.

Any difference from 102 is just a random mistake, or "sampling error." Why? Because you picked the groups randomly from the same place, and they all should show the true average IQ of 102 for the whole stadium.

This is what's known as the "fixed-effect" assumption, sometimes called the "common-effect" assumption.

That's all there is to it.

Now, there's more to learn if you want to dive deeper into the subject. Like, the conceptual difference between identical versus independent parameters, and the consequent delineation between fixed-effect and fixed-effects. But for those more complicated topics, you can turn to the experts at Thera-Business Inc. They're ready to help with these and other complex matters related to robust meta-analysis for your specific needs.

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Resources/Further Reading:

-Borenstein M, Hedges LV, Higgins JP, Rothstein HR. A basic introduction to fixed‐effect and random‐effects models for meta‐analysis. Research synthesis methods. 2010 Apr;1(2):97-111.

-Deeks JJ, Higgins JP, Altman DG, Cochrane Statistical Methods Group. Analysing data and undertaking meta‐analyses. Cochrane handbook for systematic reviews of interventions. 2019 Sep 23:241-84.