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The phrase "are robust to sample" is not correct in standard written English.
It seems to be an incomplete expression, possibly intended to convey that something is resilient or effective despite variations in sample data.
Example: "The results of the study are robust to sample variations, indicating reliability across different groups."
Alternatives: "are resilient to sample" or "are effective against sample".
Exact(1)
Our results are robust to sample selection bias, a wide array of controls, and alternative specifications.
Similar(59)
I set up the estimation problem, derive the appropriate asymptotic distribution theory as the number of clusters per stratum tends to infinity and compute asymptotic standard errors that are robust to sample-design effects.
These results show that CTFs are robust to sampling, noise and outliers.
These large components preserve true distances among variants, are well separated from each other, are robust to sampling, and are likely seeded during transmission events.
In addition, the association is robust to sample size change as the obtained results persisted when all geographical- and orientation-specific groups had equal sample size (Supplementary fig. S2, Supplementary Material online; r2 = 0.1495, P < 0.015).
The underlying assumption is that the induced cluster composition is more trustworthy if the clustering is robust to sampling variability.
Here, we infer selection by a new method, which is not confounded by clonal interference and is robust to sampling biases in our data set.
In this case, both original- and rarefied-pinniped disparity was above the 95% confidence interval for fissipeds, indicating that pinnipeds had higher variance, which was robust to sampling.> -wrap-foot> confidenceence interval.
Furthermore, while some of these similarity indices are robust to incomplete sampling and low sample effort, these require a level of data complexity that is not often available in meta-analyses of multi-taxa datasets (for example, estimation of probabilities of detection requires that the community is sampled several times and is divided into multiple spatially explicit subsamples).
These results indicate that the modelling results are robust to the sample construct.
Our results are robust to different samples and estimation methods.
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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