Short answer: meta-analyses are considered powerful because they combine results from multiple studies to estimate an overall pattern, often with more precision than any single study can provide.
A meta-analysis is usually part of a systematic review. Researchers define a question, search for relevant studies, apply inclusion criteria, assess quality, and then statistically combine results when the studies are similar enough. The goal is not simply to count papers. The goal is to understand the weight of the evidence.
Why combining studies helps
Individual studies can be small, noisy, or affected by local conditions. One study may overestimate an effect. Another may underestimate it. By combining studies, a meta-analysis can reveal whether a result is consistent across settings and how large the effect appears overall.
Meta-analyses are especially useful when studies disagree. Instead of picking the study that supports a preferred view, readers can look at the whole body of evidence. A good meta-analysis can also show where evidence is weak, where publication bias may exist, and which questions still need better research.
Why they are not automatically perfect
A meta-analysis is only as good as its question, search strategy, inclusion criteria, and source studies. If the included studies are biased, too different from one another, or poorly measured, the combined estimate can still mislead. This is sometimes called the “garbage in, garbage out” problem.
Readers should check whether the review was systematic, whether risk of bias was assessed, whether unpublished studies were considered, and whether the authors explain differences among studies. A strong meta-analysis can be one of the best forms of evidence, but it still needs critical reading.