At Precision Analytics, we’re currently working on a particularly large systematic review and meta-analysis, and we’ve come across a familiar claim in many published papers:

“Meta-analyses are inherently overpowered and will nearly always detect an effect.”

This statement is often presented as a given, but is it actually true? While it’s easy to see why people might believe it, the reality is far more nuanced.

At its core, a meta-analysis (MA) pools data from multiple studies, effectively increasing the overall sample size. In rare cases, when individual-level data are available, the increase in power can be substantial. This can lead to situations where even tiny effects reach statistical significance, reinforcing the idea that meta-analyses are “too powerful.” But does pooling always lead to higher power? Not necessarily.

While meta-analyses can sometimes enhance power, there are many situations where this assumption does not hold:

1. Heterogeneity reduces effective power

If the included studies are highly heterogeneous (e.g., differing populations, methodologies, or effect sizes), pooling them together does not meaningfully increase power. In fact, excessive heterogeneity can make results less reliable.

2. The sample size is not always large

Some meta-analyses include only a small number of studies, or rely on studies with limited sample sizes themselves. In these cases, the total effective sample size may not be much larger than an individual study.

3. Random-effects models can reduce power

Many well-conducted meta-analyses use random-effects models to account for variability between studies. These models distribute weight more evenly across studies, which often reduces power compared to fixed-effects models.

The idea that meta-analyses are always overpowered is an oversimplification. While they can sometimes detect very small effects due to large sample sizes, the presence of heterogeneity, small study counts, and appropriate statistical models means that they are not inherently overpowered.