Recruiting participants for studies of early-life longitudinal development is challenging, often resulting in practical upper bounds in sample size and missing data due to attrition. These factors pose risks for the statistical power of such studies depending on the intended analytic model. One mitigation strategy is to increase measurement precision by conducting assessments of children as close to a fixed chronological age as possible. We present analyses that illustrate how such practices are only sometimes useful, focusing on cases where temporal trajectories are analyzed using multilevel modeling approaches. Simulations were conducted using results from two studies of longitudinal development. Data were generated according to both continuous and discrete developmental processes and factorially analyzed treating time on either interval, ordinal, or categorical scales. The power to detect continuously generated developmental processes was robust to, and even benefited from, increased variability around target ages. For discrete processes, power was unaffected when modeled ordinally/categorically, but declined steadily if modeled using exact chronological age on an interval scale. Our results suggest that in many circumstances, researchers may be unnecessarily devoting resources toward minimizing age sampling variability when studying functional patterns across time. In fact, when the theoretical developmental process is continuous, increasing the age sampling variability of assessments and utilizing multilevel models in favor of latent growth curve alternatives can be associated with substantial gains rather than reductions in power. Such considerations also extend to limited equivalent formulations of other common developmental models, such as panel analysis.