The more studies a meta-analysis includes, the more likely it is to show 'statistically significant' signs of bias—even if there's no real bias, just because there are more studies.

Evidence from Studies

Supporting (1)

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P-value driven methods were underpowered to detect publication bias: analysis of Cochrane review meta-analyses.

Computational/Algorithm Study

2020 Feb

The study shows that the more studies included in a meta-analysis, the more likely these tests are to find 'bias'—even when there isn’t any—simply because of the higher number of studies.

Contradicting (0)

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No contradicting evidence found

Gold Standard Evidence Needed

According to GRADE and EBM methodology, here is what ideal scientific evidence would look like to definitively prove or disprove this specific claim, ordered from strongest to weakest evidence.

Source Study

P-value driven methods were underpowered to detect publication bias: analysis of Cochrane review meta-analyses.

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DOI: 10.1016/j.jclinepi.2019.11.011

Similar Assertions

When researchers combine few studies in a review, common statistical tests often miss signs that some results were hidden — making it look like there's no bias when there might actually be.

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83%

When scientists use certain tests to check if a review of studies is missing some results, those tests don't work as well if there aren't many studies in the review — they miss problems about a third more often when there are only a few studies.

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83%

There’s no sign that only studies showing benefits of leafy greens were published—so the results probably aren’t biased by hiding negative findings.

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39

70%