ApEn measures the logarithmic probability that nearby pattern runs remain close in the next incremental comparison [19], but allow self-counting. When dealing with stochastic processes, the analysis of conditional probabilities causes large values of \(m\) or minimal values of \(r\) to produce statistically low estimates. Ultimately, the value of the estimate depends on \(m\) and \(r\) so ApEn can vary significantly with the choice of \(m\) and \(r\). Thus, ApEn is a relative measure where the choice of input parameters needs to consider several factors, e.g., the nature of the problem being addressed, the research question, and the characteristics of the time series. [PERSON] [36] suggested that one of the advantages of ApEn is that the algorithm was finite for stochastic, noisy deterministic, and composite processes, i.e., models for complex biological systems. ApEn can differentiate between different mixed methods of deterministic and random components occurring with a different probability and is robust to outliers because the pattern formed by wild points will rarely be repeated in the waveform [7]. It has been demonstrated that greater ApEn values correspond to a more complex time series. The limitations of ApEn include that relative consistency is not guaranteed, and depending on the value of \(r\), the ApEn values will change [1]. Additionally, the value of ApEn depends on the length of the data series. Lastly, the self-counting aspect of the algorithm creates a statistical bias that particularly impacts situations with small data sets, which is when only a few or even no matches are present, the entropic result is biased toward zero [1]. Acknowledging the ground-breaking work of [PERSON] [22] and [PERSON] [26] from the 1980s, [PERSON] and [PERSON] [6] introduced SampEn, which differs from ApEn in the following ways: (1) SampEn does not allow self-counting, (2) SampEn takes the logarithm of all the times two sequences are similar, and (3) ApEn is dependent on the size of the series, whereas SampEn is not [1]. They believed that SampEn counteracted the limitations of ApEn, claiming that SampEn, as a statistical alternative, solved the self-counting problem eliminating the bias associated with ApEn. The use of SampEn appears to quantify regularity more effectively and eliminates many of the problems associated with ApEn [6]. SampEn maintains the relative consistency and is also mostly independent of the length of the time series [6]. SampEn was created to address the bias and inconsistencies of ApEn, yet both methods retain similarities [6], except in the case of strong multifractality. There is no consensus on which method is preferable, but one's choice should be dependent on the research question and time series being evaluated. When using ApEn and SampEn important consideration must be given to parameter selection, as these choices may have the greatest impact on the final entropy value even in the presence of noise [38]. Previously, we discussed how ApEn and SampEn were used to distinguish quiet standing postural control between the young and elderly [2], between healthy and concussed athletes [18; 19], and between eyes-open and eyes-closed conditions [13]. However, in each case, the researchers chose specific values for parameters \(N\), \(m\), and \(r\), without the examination of and prior evaluation of parameter choices. It has been shown that given a time series with \(N\) data points, the calculation of entropy requires a priori determination of two unknown parameters, embedding dimension, \(m\), and threshold, \(r\)[39]. Multiple pairings of parameter selections allow one to examine relative consistency where a better discrimination capacity can be accomplished. Incorrect parameter choice, and lack of due diligence in selecting \(m\), \(r\), and \(N\) can undermine the interpretation and application of entropy results, as shown by previous investigators [8; 12; 15]. According to [PERSON] et al.