We present the results of a study comparing the performance of younger adults (YA) and people in late adulthood (PLA) across ten low-level analysis tasks and five basic visualizations, employing Bayesian regression to aggregate and model participant performance. We analyzed performance at the task level and across combinations of tasks and visualizations, reporting measures of performance at aggregate and individual levels. These analyses showed that PLA on average required more time to complete tasks while demonstrating comparable accuracy. Furthermore, at the individual level, PLA exhibited greater heterogeneity in task performance as well as differences in best-performing visualization types for some tasks. We contribute empirical knowledge on how age interacts with analysis task and visualization type and use these results to offer actionable insights and design recommendations for aging-inclusive visualization design. We invite the visualization research community to further investigate aging-aware data visualization. Supplementary materials can be found at https://osf.io/a7xtz/.