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Tajima's D estimates were non-significant and negative, except the two with the lowest sample sizes (Table S4).
The percent contributions of 3- and 4-parameter models to the model averages, indexed by the 5 datasets, were (.04, 90, 100, 80, 100) and (97, 10, 0, 20, 0), respectively, i.e. the 1st dataset requires 4-parameter models and the 3rd and 5th datasets (with lowest sample sizes) require only 3-parameter models.
Our power to detect distortion strength of 7.7% at α = 0.05 is 70.4% with a sample size of 261 and 73.9% with a sample size of 283 (the two lowest sample sizes for sperm genotyping), suggesting that we do not lack power to detect distortion in sperm unless the true distortion strength is substantially lower than estimated here.
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The range of AR was 4.85-13.50 (rarestimate estimate for the lowest sample size N = 11).
Omitting islands with low sample sizes also reduced the potential influence of specimens that may have erroneous locality data.
We combined markers using approaches that did not require statistical fitting because of the low sample sizes.
To avoid low sample sizes, the study samples were pooled.
This generated a higher bias of for low sample sizes.
However, low sample sizes, and disparate observations make this difficult.
This effect was exacerbated at low sample sizes.
The results were inconsistent partly due to low sample sizes.
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CEO of Professional Science Editing for Scientists @ prosciediting.com