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All spectra seem to follow a specific pattern, and no significant deviation can be detected for any sample.
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Similarities between local stiffnesses and deviations can be detected and quantified by a higher-order polynomial, making it easier to predict deviations.
Testing with synthetic data and real imagery captured at three different sites shows that major deviations can be detected and lower bounds for the magnitudes of the deviations estimated from a single image only.
We showed that significant deviations can be detected for the majority of these protein families.
As most model fit statistics are sensitive to sample size, statistically significant outcomes may be trivial (models never fit the data perfectly, and even the tiniest deviations can be detected by increasing sample size).
Assuming a standard deviation of 0.7 mmol/l, this difference can be detected with a power (1-β) of 0.80 and an alpha of 0.05 with two groups of 64 subjects.
However, considering (especially local) microscopic structures,[ 13, 14] deviations from ideality can be detected.
Paradoxically, one consequence of large sample sizes is that even small deviations from linearity can be detected, which again leads to categorisation of the originally continuous variables, resulting in loss of power and the need for larger sample sizes.
This sample size is also sufficient, with the same power, to test for DIF where a difference of 0.5 standard deviations within the residuals can be detected for any two groups.
Deviations from Gaussian distributions can be detected or measured by using higher than second order moments.
Periodicity of this rhythmic change in activity can be accurately measured using a number of statistical tools for the analysis of biological rhythm, and deviations from a circadian profile can be detected with ease.
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