Sammenlign metoder

	Planlagt Kontrastanalyse	Bonferroni-korrektion	Envejs variansanalyse
Fagområde	Statistik	Statistik	Statistik
Familie	Hypothesis test	Hypothesis test	Hypothesis test
Oprindelsesår	2000	1961	1925
Ophavsperson	Rosenthal, Rosnow & Rubin (modern formalization)	Carlo Emilio Bonferroni; formalized for multiple comparisons by Olive Jean Dunn	Ronald A. Fisher
Type	Parametric planned comparison	Family-wise error rate (FWER) correction	Parametric mean comparison
Oprindelig kilde	Rosenthal, R., Rosnow, R. L. & Rubin, D. B. (2000). Contrasts and Effect Sizes in Behavioral Research: A Correlational Approach. Cambridge University Press. ISBN: 978-0521659802	Bonferroni, C. E. (1936). Teoria statistica delle classi e calcolo delle probabilità. Pubblicazioni del R Istituto Superiore di Scienze Economiche e Commerciali di Firenze, 8, 3–62.	Fisher, R. A. (1925). Statistical Methods for Research Workers. Edinburgh: Oliver and Boyd.
Aliasser	planned comparisons, planned contrasts, a priori contrasts, Kontrast Analizi — Planlanmış Karşılaştırmalar	Bonferroni adjustment, Bonferroni method, Bonferroni procedure, FWER correction	one-factor ANOVA, single-factor ANOVA, analysis of variance, tek yönlü ANOVA
Relaterede	2	5	4
Resumé	Planned contrast analysis is a parametric hypothesis-testing method that evaluates specific, theoretically motivated comparisons among group means — comparisons that the researcher specifies before data collection, not in response to observed patterns. Formalized comprehensively by Rosenthal, Rosnow, and Rubin (2000), the approach assigns a set of contrast coefficients to the groups being compared, with the constraint that the coefficients sum to zero, and then tests whether the resulting weighted combination of means differs significantly from zero.	The Bonferroni correction is a conservative, universally applicable method for controlling the family-wise error rate (FWER) when conducting multiple simultaneous hypothesis tests. Grounded in Bonferroni's 1936 probability inequality and formalized for multiple comparisons by Olive Jean Dunn in 1961, the procedure divides the target significance level α by the number of tests m, ensuring that the probability of making even one false rejection across the entire family of tests does not exceed α.	One-way ANOVA is a parametric hypothesis test that compares the means of three or more independent groups on a single continuous outcome to decide whether at least one group mean differs. It rests on the variance-partitioning framework introduced by Ronald A. Fisher in 1925.