The association between adiponectin and mortality in this smaller sample was similar to the larger analytic sample: subjects in the fourth adiponectin quartile had an increased hazard of death compared with those in the first quartile (fully adjusted HR = 3.4 [95% CI: 1.4 8.3]).
As the differences between the samples were small (differences between the two samples for all categories are under 5%%) and we considered that they were unlikely to affect results to a great extent, we did not adjust for them in the statistical analysis.
Because agriculture reduces spatial heterogeneity in soil properties, we examined the hypothesis that soil microbial activity in natural wetlands would show a high degree of spatial autocorrelation when distances between samples were small; however, in wetlands that were disturbed by agriculture these spatial patterns would occur across larger sampling distances or disappear due to homogenization.
The distances calculated between individual blood samples were smaller than those observed between different tissues or brain regions.
Although the pattern of developmental expression changes in the microarray data was clear, the differences between individual samples were in most cases small, and we were unable to replicate the template matching results using QPCR due to the increased amount of variability in this dataset.
However, the absolute differences between linked and unlinked samples were small, and have been reported elsewhere [ 22].
In Eq. 15, the similarities between negative samples are small, so we let w be constant.
In the meantime, I refer Stattin, Kaaks and all interested readers to the article by Altman and Bland (1995), which eloquently explains the pitfalls of concluding clinical insignificance when differences between sets of small sample size are found to be statistically nonsignificant.
Overestimation of R h was also observed, and the variation of the overestimation between PSi samples was smaller.
