Estimating how long a task will take to complete (i.e., the task duration) is important for many applications, including calendaring and project management. Population-scale calendar data contains distributional information about time allocated by individuals for tasks that may be useful to build computational models for task duration estimation. This study analyzes large-scale calendar appointment data from hundreds of thousands of individuals and millions of tasks to understand expected task durations and the longitudinal evolution in these time estimates. Machine-learned models are trained using the appointment data to estimate task durations. Study findings show that task attributes, including content (appointment subjects), context, and history, are correlated with time allotted for tasks. We also show that machine-learned models can be trained to estimate task durations, with multiclass classification accuracies of almost 80%. The findings have implications for understanding time estimation in populations, and in the design of support in digital assistants and calendaring applications to find time for tasks and to help users, especially users who are new to a task, block sufficient time for task completion.