Сравнение методов

	Публикационная предвзятость	Effect Size
Область	Статистика исследований	Статистика исследований
Семейство	Process / pipeline	Process / pipeline
Год появления	1979	1988
Автор метода	Robert Rosenthal	Jacob Cohen
Тип	Concept	Concept
Основополагающий источник	Rosenthal, R. (1979). The file drawer problem and tolerance for null results. Psychological Bulletin, 86(3), 638–641.	Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences (2nd ed.). Lawrence Erlbaum Associates. ISBN: 0-8058-0283-5
Другие названия	file drawer problem, selective reporting, outcome reporting bias, funnel plot asymmetry	ES, Cohen's d, standardized effect, practical significance
Связанные	4	4
Сводка	Publication bias occurs when the results of a study influence whether the study is published. Typically, studies with statistically significant or positive results are more likely to be published than studies with non-significant or negative results, even if both are scientifically valid. This bias distorts the published literature, making treatments appear more effective than they actually are. Rosenthal (1979) termed this the 'file drawer problem': research with null results sits in file drawers, unpublished, creating a biased sample of published evidence. Funnel plots and statistical tests (e.g., Egger test) can detect asymmetry suggesting publication bias; meta-analyses must account for this bias.	Effect size quantifies the magnitude of a research finding independent of sample size. While a p-value tells you whether a result is statistically significant, an effect size tells you how big the result is. Jacob Cohen formalized effect size measurement in behavioral sciences (1988), establishing standard benchmarks (small = 0.2, medium = 0.5, large = 0.8 for Cohen's d). Effect sizes are essential for meta-analysis, power analysis, and communicating the practical importance of research findings.