Longitudinal measurement is increasingly used to quantify the effects of recent and ongoing influences on lung function, whether treatments of groups of patients, or exposures of working or community populations. The variability in an estimate, eg, mean annual change in FEV1, comes from two sources; variability from true differences in annual change among individuals (called signal), and variability from measurement error (called noise). Signal is useful variability, potentially relatable to explanatory variables, and noise is extraneous. Assuming the variance of true differences remains constant, any increase in noise produces a calculable fall in the proportion of signal in the total observation, which fall we term "signal decay". This is not a function of the number of individuals, which influences rather the statistical power to determine that observed differences are not likely from chance alone. Imprecision in the estimation of individuals' rates of change is a major source of signal decay. Within practical limits, this can be compensated for by increasing the length of the study. Higher rates of subject attrition cause signal decay, in addition to loss of statistical power and susceptibility to survivor bias. Increasing the frequency of testing, within a study of constant length, has little effect on signal and noise, but interval testing protects against secular bias and minimizes data loss from subject attrition.