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This sample size enables a good characterization of AKI in HH as compared to AKI in patients without HH.
The chosen sample size enables us to identify changes in the primary outcome of 20% between the groups.
This is probably because a larger sample size enables closer matches to be found for individuals with missing X.
This sample size enables to identify a reduction of 19 ADEs or more from the expected 38 ADEs in 248 admissions during the pre-measurement period.
A large sample size enables the detection of subtle effects of the miRNA of interest and comparisons of the relative efficacy of different miRNAs.
This high temporal resolution, in combination with a large sample size, enables us to detect subtle alterations in gene expression dynamics that would have been missed by traditional, qualitative RNAi analyses.
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Finally, based on our model estimates we were able to determine the required number of eggs to count for each sample size, enabling investigators to optimize the probability of correctly classifying a theoretical TDE while minimizing both financial and technical resources.
This sample size enabled models of tree height vs LiDAR height to be estimated with a relative root mean squared error (RMSE) of 5%.
Yet, the richness of the data on educational experiences and outcomes, coupled with the recentness of the cohort and our large sample size, enable us to learn a great deal about postsecondary outcomes and their determinants.
Although we appreciate the peril in including only two participants, the small sample size enabled us to provide a detailed account of how the various measures obtained from keystroke logging can be combined with other types of data (e.g. final product, interview protocol) to explicate the test takers' reading-into-writing processes.
The high resolution of the SNP array and the large sample size enabled us to monitor the small DNA copy number changes occurring in NPC.
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CEO of Professional Science Editing for Scientists @ prosciediting.com