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The assumptions of ANOVA that (i) error variances are equal and (ii) the residuals of the model are normally distributed generally do not hold for DNA microarray data.
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We find that Type I error increases gradually with increased linkage.
They also show that type-I error, the economically costliest error, is the greatest beneficiary of this gain in stability.
Results show that Type I error rates are conservative in the Bayesian framework and that there is more power for the fit indices in a frequentist framework.
Our results also ensure that type I error is controlled at the level of the randomization test for adaptive stratification designs used for balancing covariates.
This demonstrates that type I error rates are inflated and consequently not controlled.
Null model simulations established that type I error rates are correctly controlled for permutation-based thresholds (Cheng and Palmer 2013).
Simulated Type I errors were very similar to expected, showing that Type I error is well controlled for all three metrics – single visit, Average of up to three visits and SHAVE of up to three visits (Supplementary Materials Table S1).
This comparison reveals that: i) relative errors are lower than 13% and ii) CPU times decrease significantly, more than one order of magnitude.
After that, I made two alignment errors in a row and ended up with more wasted space.
My silly happy dances are my way of conveying that it is only through making errors that I have had the opportunity to learn and grow.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com