Publication bias can be detected through sample-size and effect-size correlations.

Evidence

Other rough, initial tests involve the relationship between sample size and effect size. 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

• DEFINE: Explains publication-bias detection via sample-size correlations and funnel graphs.
• XREF: Connects to standard meta-analytic methods the reader likely encounters across quantitative literatures. — connects to: Industry cherry-picking is continuous with ordinary scientific publication bias — Cailin O’Connor and James Owen Weatherall, The Misinformation Age: How False…, 359; The intergroup contact-prejudice link survives file-drawer checks, indicating limited publication bias. — Thomas F. Pettigrew, Linda R. Tropp, A Meta-Analytic Test of Intergroup…, 104
• recurring: this claim recurs 2 times within its source

Related

• A null correlation between sample size and effect size indicates minimal publication bias. (similarity 0.94)
• The intergroup contact-prejudice link survives file-drawer checks, indicating limited publication bias. (similarity 0.87)
• Comparing published and unpublished studies tests whether reporting bias inflates effects. (similarity 0.86)
• Meta-analyses overestimate effect sizes because null results go unpublished. (similarity 0.84)
• Trim-and-fill and funnel graph methods tend to overestimate publication bias. (similarity 0.84)
• The contact-prejudice effect may be partly an artifact of publication bias. (similarity 0.82)
• Industry cherry-picking is continuous with ordinary scientific publication bias (similarity 0.80)
• Publication bias inflates the apparent effectiveness of prejudice-reduction programs (similarity 0.78)
• The trim-and-fill adjustment increases the contact-prejudice effect estimate, supporting its robustness. (similarity 0.78)
• Publication bias inflates apparent effectiveness of intergroup programs (similarity 0.78)