It is argued that: if linear trends are present in heart rate (HR) data, due to sinus arrhythmic variation and the gradual nature of the HR response to an external treatment, the time‐series statistic successive difference mean square (SDMS) will be a logically more appropriate estimate of HR variability than is variance. A fifty‐subject study assessed both the empirical import of this argument as well as the question of the appropriate scale of measurement, i.e., either beats per minute (BPM) or interbeat intervals (IBI). Separate analyses to determine changes in HR variability from baseline to treatment conditions were carried out using each combination of statistic and scale to define HR variability. These analyses showed that HR variability either did not change or was significantly decreased by the treatment depending on the combination of statistic and scale used to define HR variability. This differential result, depending on the type of analysis, was due to the fact that nonrandom linear trends in the HR data spuriously inflated variance relative to SDMS and affected the BPM scale significantly more than the IBI scale. The major result from this study is that the combination of SDMS as the variability statistic, and IBI as the scale of measurement for HR, is both the best and most appropriate technique for assessing changes in HR variability. In addition, the IBI scale should be considered when analyzing mean HR scores if differential linear trends exist between conditions being compared, as in this case, since the IBI scale should increase statistical precision for detecting differences between mean HR scores during those conditions. Finally, when the more appropriate SDMS statistic and IBI scale define HR variability, this study supports the claims of previous investigators who have reported that increases in cognitive functioning led to decreases in HR variability. Copyright © 1979, Wiley Blackwell. All rights reserved
