Sentence examples for more powerful sample from inspiring English sources

We had to decide whether we were going to use modern statistical techniques like multiple imputation to replace missing values in order to create less biased estimates of accuracy and obtain more powerful sample sizes for external validation (Vergouwe et al, 2010).

This is an interesting area of research and one that should be considered in future research with more powerful samples, although at present there is little evidence for gene × sex interaction, at least in symptoms of overactivity [ 22].

However, there were no similar cases in the much more powerful lymphocyte sample.

This was done because MDR is more powerful per sample than MDR-PDT and increasing the size of the search space provided observations for MDR that were not near 100% power.

However, the R-value for this relationship is only 0.738, and the more powerful paired samples t-test shows a (borderline) significant bias (p = 0.04).

Nevertheless, because of the importance of testing the TCD model and given the expected creation of neuroimaging data bases such as TINNET, we hope that more powerful large-sample studies will be carried out in the near future.

(i) The two CUSUM tests based on the Bartlett kernel and the quadratic spectral kernel become more powerful as the sample size T becomes larger.

For this reason, it is possible that the findings would have been even more powerful in a sample of children and adolescents with type 1 diabetes with a broader range of ethnic diversity, educational levels, and executive functioning competence.

Several recent studies that have combined unrelated subjects and nuclear families to form an enlarged sample [26] [31] have demonstrated that analyzing combined samples can be more powerful than analyzing individual samples separately.

The power of these tests also depends on sample size, and the R2 test is more powerful for a small sample while Fs is more powerful for a large sample [ 41].

This model is more powerful than the sample-based model in detecting departures between observed and null partitions because of intra-specific aggregation of individuals.