By spiking with human genomic DNA we hoped to approximate more realistic sample environments for which the assay was constructed.
Power analyses based on this very large sample size produce non-centrality parameters which can subsequently be used to calculate power for other, more realistic sample sizes.
With these advances, the proposed method should become suitable for the determination of n J(H,C) values under more realistic sample conditions.
The non-centrality parameter obtained in the analysis of the large simulated data set can subsequently be used to calculate the power for smaller, more realistic sample sizes.
We see this as positive because discussion would likely lead to more realistic sample size calculations – an outcome that would be of great benefit for the field of complementary and alternative medicine.
For more realistic sample size calculation and power analysis, the impact of unmeasured aetiological determinants and the quality of measurement of both outcome and explanatory variables should be taken into account.
Evidence-based effect size estimation helps justify assumptions in light of empirical evidence and can lead to more realistic sample size calculations, an outcome that would be of great benefit for the field of complementary and alternative medicine.
Nonetheless, the findings presented here constitute a significant improvement with respect to reference sample preparation of more realistic water samples.
A similar reproducibility evaluation was also performed on a more realistic biological sample.
The use of several combinations of universal and/or specific primers, coupled with the use of a range of different PCR conditions, might allow a more realistic qualitative sample of the diversity of organisms present in a given biotope, although this would never be guaranteed.
