In general, the observed differences between iASeq and the d, z, b and B statistics could be caused by many factors such as use of different statistical models, ranking statistics, or methods for parameter estimation.
Most variety in performance resulted from differing approaches to data quality and the use of different statistical tests.
The main objective of the study was to illustrate the use of different statistical methods to analyze the effects of prognostic/predictive factors on DSS at differing time points.
As a note of caution, the difference between plant and animal studies may be caused by differences in methodology, i.e. different crossing designs and the use of different statistical packages for estimating phenotypic variance.
Differences between our results and previous studies [1, 2, 7, 19], which demonstrated an association between RBC transfusion and infection, may be explained by the use of different statistical analysis methods.
The use of different statistical measurements (RMS, kurtosis and skewness) together with the analysis by frequency bands allows identifying the tool condition and the transition between wear conditions (progressive, intermediate and advanced).
These data indicate that the use of different statistical models in the face of increased variation, as well as different microarray platforms [34], can affect the results of microarray analyses of polyploid transcriptomes.
The results of the use of different statistical techniques show largely the same results.
Here, the use of different statistical criteria led to the same model.
It is also possible that the use of different statistical methods for analysis of microarray data my yield widely different estimates of the frequency of regulated genes.
