[17], when the sampling rate is too high, i.e., frequency collection rates greater than 1000 Hz, redundancy likely exists within the data, which tends to artificially reduce entropy values [21]. Redundancy, i.e., repetitiveness of, or repeating, values, results in smaller entropy values and more signal regularity due to the counting of repeated matches. The redundant data problem can be solved by downsampling overly redundant time-series data sets. Although downsampling removes real data, sensitivity analyses have demonstrated that the removal of some data does not impact the subsequent application of the revised data set [12; 16]. Previous studies demonstrated the link between entropy and sampling rate. [PERSON] et al. [40] simulated different sampling rates by resampling ankle joint angle time series and found that higher sampling rates significantly reduced ApEn values. Conversely, results from [PERSON] et al. [41] suggest that excessive downsampling, e.g., to 25 Hz, artificially altered the standing center-of-pressure displacement and velocity SampEn values. Few studies have examined the effect of different values of \(N\), \(m\), and \(r\) on the calculated values of ApEn and SampEn. [PERSON] and [PERSON] [6] in their comparison of ApEn and SampEn selected constant \(m\) (of 2) and demonstrated differences in both entropies across different \(N\)'s and \(r\)'s in data unrelated to standing postural control. [PERSON] et al. [17] showed that both ApEn and SampEn were sensitive to short data sets (\(N\leq 200\)) and parameter choices after subjecting theoretical and experimental (i.e., gait) data to all combinations of \(m\) = 2, 3, and 4, and \(r\) = 0.05, 0.1, 0.15, 0.20, 0.25, and 0.30 times the standard deviation of the entire time series; and \(N\) = 100, 120, 140, 160, 180, and 200. In a follow-up study on gait (over ground and treadmill conditions) data, [PERSON] et al. [16] again compared ApEn and SampEn using different combinations of \(N\), \(m\), and \(r\), but with much larger time series (i.e., \(N\)) and \(r\) constant (in addition to \(r\)\(\times\) standard deviation). They found that when \(r\) was constant, SampEn demonstrated overall excellent relative consistency for all combinations of \(r\), \(m\), and \(N\) and that when \(r\) constant was used, overground walking was more regular than treadmill, but treadmill walking was more regular when using \(r\)\(\times\) SD for both ApEn and SampEn. According to [PERSON] and co-workers [16,38], care should be taken when selecting the input parameters of tolerance window, \(r\), vector length \(m\), and time-series length \(N\). However, there is a paucity of research on the selection of input parameters involving the analysis of COP time series of quiet standing, particularly related to the postural control of tandem stance. Based on our review of the literature, it is clear that the appropriate selections of \(N\), \(m\), and \(r\) are critical for computing ApEn and SampEn values that can enhance the interpretation of inherently non-linear biological time series. Therefore, the purposes of this research were to (1) examine and evaluate the effect of altering input tolerance, \(r\) (with the constant embedding dimension, \(m\) = 2) on ApEn and SampEn values related to the center-of-pressure time series during quiet standing postures, i.e., feet together and tandem standing, under eyes-open and eyes-closed conditions, and (2) assess which entropy measure was less biased and most consistent.