A null correlation between sample size and effect size indicates minimal publication bias.

Evidence

We can test for the possibility of publication bias in several ways. Two preliminary methods are simply to correlate sample sizes with effect sizes and to develop a funnel graph consisting of a scatter diagram with the two variables (Light & Pillemer, 1984). A nonsignificant correlation and a symmetrical funnel graph each suggest minimal publication bias. — Thomas F. Pettigrew, Linda R. Tropp, A Meta-Analytic Test of Intergroup…, 47

Categorization

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Related

• Publication bias can be detected through sample-size and effect-size correlations. (similarity 0.94)
• Meta-analyses overestimate effect sizes because null results go unpublished. (similarity 0.85)
• The intergroup contact-prejudice link survives file-drawer checks, indicating limited publication bias. (similarity 0.83)
• Trim-and-fill and funnel graph methods tend to overestimate publication bias. (similarity 0.81)
• Comparing published and unpublished studies tests whether reporting bias inflates effects. (similarity 0.80)
• Large samples produce significant correlations that explain little variance (similarity 0.79)
• The trim-and-fill adjustment increases the contact-prejudice effect estimate, supporting its robustness. (similarity 0.79)