A meta-analysis is a statistical method that combines results from multiple studies addressing the same or a closely related research question to produce an overall estimate of an effect.
Rather than treating each study separately, researchers convert compatible results to a common measure and statistically combine them to estimate the overall size and direction of an effect across the available evidence.
Meta-analyses are commonly used in medicine, psychology, education, social science, public health, and other fields where multiple studies have examined the same question.
How do meta-analyses work?
Researchers identify studies that report comparable outcomes, extract the relevant data, and use statistical methods to combine the results.
A meta-analysis may involve:
Selecting studies that address a similar research question
Extracting effect sizes, estimates, and other relevant data
Assessing differences between studies
Weighting studies based on factors such as sample size or precision
Combining results into an overall estimate
Measuring how much results vary across studies
Exploring possible reasons for differences between study findings
For example, researchers might combine results from several trials testing the same treatment, compare the effects of an educational intervention across multiple studies, or estimate the overall association between an exposure and an outcome.
Strengths of meta-analyses
Meta-analyses can:
Combine evidence from multiple studies
Provide a more precise estimate than many individual studies can provide on their own
Increase statistical power
Help clarify findings when individual studies reach different conclusions
Help identify patterns across studies
Examine whether results differ across populations, settings, or study characteristics
Summarize a large body of quantitative evidence in a single analysis
Meta-analyses can help researchers understand the overall direction and magnitude of an effect across multiple studies.
In clinical and biomedical research, well-conducted meta-analyses are often considered a strong form of evidence because they statistically combine findings across multiple studies. They are commonly placed near the top of evidence hierarchies.
Limitations of meta-analyses
The conclusions of a meta-analysis depend on the quality and comparability of the studies it includes.
Differences in study populations, designs, interventions, measurements, or methods can make results difficult to combine or interpret. If studies are too different from one another, combining their results may not be appropriate.
Meta-analyses may also be affected by publication bias if studies with certain results are more likely to be published or included.
Poor-quality, biased, duplicated, or selectively reported studies can also distort the pooled result. Combining several studies does not eliminate weaknesses in the underlying research, so the quality of the included studies and the methods used to select and analyze them remain important.