Summary
Combines results from many studies to estimate an overall effect size.
In depth
Combines effect sizes from multiple studies, weighting by precision, with fixed or random effects models. Forest plots display them; funnel plots check publication bias. Heterogeneity is measured by I².
Quality of included studies matters. Heterogeneity should be explored. Publication bias can skew results.
Key idea: pooling studies increases power and shows consistency.
A meta-analysis statistically combines results from multiple independent studies on the same question to estimate an overall effect size with greater power and precision. It typically follows a systematic review: a protocol with defined search methods, inclusion criteria and quality assessment.
Effects are weighted by study precision (fixed or random effects models), heterogeneity is measured with I-squared, and forest plots display each study's estimate and the pooled result. Funnel plots check for publication bias, as studies with null results are less likely to be published. Garbage in, garbage out applies.
Example: Cochrane reviews pool clinical trials to guide treatment.
Key idea: combining evidence gives a clearer picture than any single study, though only if it is done carefully.
Frequently asked questions
What is Meta-Analysis?
Combines results from many studies to estimate an overall effect size.
Which area of biology covers Meta-Analysis, and how advanced is it?
Meta-Analysis is one of the 51 topics in Lab Methods & Biostatistics. It is rated Advanced, so it suits learners who want specialist depth. Related topics in this area include Statistical Power, Experimental Design, Control Group.
Can you give an example of Meta-Analysis?
Cochrane reviews of medical interventions.
What should I learn before Meta-Analysis?
Work through the Beginner topics in Lab Methods & Biostatistics first, such as Scientific Method, Hypothesis, Variables, then return to Meta-Analysis. Advanced topics assume the Intermediate vocabulary.
What topics are related to Meta-Analysis?
The closest topics to Meta-Analysis are Statistical Power, Experimental Design, Control Group, Reproducibility in Science. Comparing them side by side is a good way to see how the ideas connect.
How can I check that I understand Meta-Analysis?
Read the summary and the in-depth section, then use the quick check on the interactive lesson for Meta-Analysis. To practise the whole of Lab Methods & Biostatistics, use the Lab Methods & Biostatistics quiz.
How is Meta-Analysis different from Statistical Power?
Meta-Analysis: Combines results from many studies to estimate an overall effect size. Statistical Power: The probability of detecting a true effect, determined by sample size, effect size and variability. Read both side by side and note what each one explains.
How should I study Meta-Analysis step by step?
First, read the summary and write the definition in your own words. Second, read the in-depth section and note any example. Third, compare it with Statistical Power and explain the difference aloud. Finally, take the Lab Methods & Biostatistics quiz and come back to this page for any question you miss.