Porovnat metody

	Publikační zkreslení	Testování nulové hypotézy
Obor	Statistika ve výzkumu	Statistika ve výzkumu
Rodina	Process / pipeline	Process / pipeline
Rok vzniku	1979	1925
Tvůrce	Robert Rosenthal	Ronald Fisher; Neyman & Pearson
Typ	Concept	Concept
Původní zdroj	Rosenthal, R. (1979). The file drawer problem and tolerance for null results. Psychological Bulletin, 86(3), 638–641.	Fisher, R. A. (1925). Statistical Methods for Research Workers. Oliver and Boyd.
Další názvy	file drawer problem, selective reporting, outcome reporting bias, funnel plot asymmetry	NHST, hypothesis formulation, null hypothesis, alternative hypothesis
Příbuzné	4	4
Shrnutí	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 Rosenthal (1979) termed this the 'file drawer problem': research with null results sits in file drawers, unpublished, creating a biased sample of published evidence.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.	Null Hypothesis Significance Testing (NHST) is the dominant statistical framework in empirical research. The null hypothesis (H₀) represents the default assumption—typically 'no effect' or 'no difference'—while the alternative hypothesis (H₁) represents the claim being tested. The test calculates the probability of observing the data given H₀ is true (p-value); if p is very small, H₀ is rejected in favor of H₁. Formulated by Ronald Fisher and extended by Neyman and Pearson in the early 20th century, NHST is foundational to confirmatory research but has been widely critiqued for misuse and misinterpretation.