K-fold is chosen because of the low number of samples.
The investigations were carried out with a very low number of samples.
Due to the low number of samples, no statistical analysis was performed, and all following observations refer to trends.
Clearly, we are able to represent EEG signals with a low number of samples and reconstruct them with high fidelity.
Results on both analytical and engineering test cases show the efficiency of the approach in terms of accuracy and low number of samples.
The percent bone ingrowth in the penetration zone was higher in the orthogonal channel design but not statistically different due to the low number of samples.
The relatively low number of samples results in large confidence interval (CI) in particular regarding the comparison of AUC values by ROC analysis.
The results show stable performance of established classifiers and efficient (low number of samples requested) as well as fast integration (steeply rising accuracy curves) of new event types (classes).
It must be taken into account that, due to the different kind of data and overall to their big mole, a traditional univariate data treatment would be very difficult even in the case of a low number of samples.
Considering the relatively low number of samples, the type of data and their increased variability, especially in the sub-scales, the Mann–Whitney nonparametric test was also applied, which fully confirmed the statistical results of the t-test.
However, these results were based on a low number of samples and the length of the animals' itineraries was only occasionally recorded by an observer from a greater distance.
