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.

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.

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.

The studies didn’t seem to be hiding negative results, but the smaller ones showed odd patterns that might mean some results weren’t fully reported.

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.