Results 21 to 30 of about 30,603,790 (296)

New metrics for multiple testing with correlated outcomes

Frontiers in Applied Mathematics and Statistics, 2023
When investigators test multiple outcomes or fit different model specifications to the same dataset, as in multiverse analyses, the resulting test statistics may be correlated.
Maya B. Mathur, Tyler J. VanderWeele
doaj   +1 more source

Type-I error rates.

, 2014
Type-I error rates.
Xiang-Yang Lou (228769), Wan-Yu Lin (91498), Guimin Gao (46470), Nianjun Liu (20391)   +3 more
core   +2 more sources

Bilimsel Çalışmalarda P-Değerinin Rapor Edilmesi P

Journal of Agricultural Sciences, 2005
Bilindiği üzere bilimsel çalışmaların sonuçları genellikle P-değerinden yararlanılarak rapor edilmektedir.
Serkan Subaşı, Mehmet Mendeş, Ensar Başpınar   +2 more
doaj   +1 more source

Uygun Simulasyon Sayısının Belirlenmesi: Monte Carlo Simülasyon Çalışması

Journal of Agricultural Sciences, 2005
Biyolojik, sosyal, ziraat, tıp ve ekonomik olayların modellenmesinde simülasyon teknikleri oldukça önemlidir. Dolayısıyla, denemenin planlanması aşamasında kaç simülasyon denemesinin yapılacağı sorusu oldukça önemli bir sorudur.
Mehmet Mendeş
doaj   +1 more source

Testing hypotheses under covariate-adaptive randomisation and additive models

Statistical Theory and Related Fields, 2018
Covariate-adaptive randomisation has a long history of applications in clinical trials. Shao, Yu, and Zhong [(2010). A theory for testing hypotheses under covariate-adaptive randomization. Biometrika, 97, 347–360] and Shao and Yu [(2013).
Ting Ye
doaj   +1 more source

Familywise type I error of ANOVA and ANOVA on ranks in factorial experiments

Ciência Rural, 2022
: This research evaluated the importance of a preliminary general analysis of variance (ANOVA) in the interpretation of data from factorial experiments under total nullity. For this, we evaluated the familywise type I error rate (accumulated FWER) of the
André Mundstock Xavier de Carvalho, Matheus Romano de Souza, Tadeu Bernardes Marques, Davi Leite de Souza, Emanuel Fernando Maia de Souza   +4 more
doaj   +1 more source

Type I Error Rates are Not Usually Inflated

Journal of Trial and Error
The inflation of Type I error rates is thought to be one of the causes of the replication crisis. Questionable research practices such as p-hacking are thought to inflate Type I error rates above their nominal level, leading to unexpectedly high levels ...
Mark Rubin
doaj   +1 more source

Assessing the impact of differential genotyping errors on rare variant tests of association. [PDF]

PLoS ONE, 2013
Genotyping errors are well-known to impact the power and type I error rate in single marker tests of association. Genotyping errors that happen according to the same process in cases and controls are known as non-differential genotyping errors, whereas ...
Morgan Mayer-Jochimsen, Shannon Fast, Nathan L Tintle   +2 more
doaj   +1 more source

Type-I Error Rate Evaluations.

, 2015
Type-I error rates for 0.05-level NB2 and NBP GOF tests based on squared vertical distance (“Sq.Vert.D.”) or Pearson statistics (“Pear.Stat.”), from 1,000 simulated samples from each of several conditions.
Yanming Di (91480), Daniel W. Schafer (202090), Gu Mi (129656)   +2 more
core   +1 more source

Type I: families, planning and errors [PDF]

Advances in Physiology Education, 2012
Scientists frequently want to answer the question ‘has this treatment had an effect?’ Most are unaware that the tests they usually use do not directly address this question. These tests usually pose a different question, based on the possibility that nothing has happened.
Gordon B, Drummond, Sarah L, Vowler
openaire   +8 more sources