At this point, let us address the differences between error margins and convergence reported by SEEKR2 calculations. The apparent discrepancy between these two concepts can be clearly observed in figures 2-4, where SEEKR2 appears to estimate the rate constants with very high precision, but the estimated quantities appear to bear differences from one another. Error margins mainly depend on the shape of distributions of rate matrices, where the likelihood of a matrix is computed based on the statistics, counts, and times of transitions between milestones observed in simulation (The details of this computation may be found in the SI). In contrast, the convergence of results depends heavily on how well sampled are the relevant states in a simulation. A system may transition between a series of energy basins, and all of these regions must be sampled thoroughly if good convergence is to be expected. Therefore, one many imagine the hypothetical situation where many transitions between milestones have been observed, but there was sufficient time in the trajectory to sample only one of multiple important energy basinsyielding small error margins, but wide convergence errors. SEEKR2 includes additional tools, such as converge.py, that allow users to better analyze the quality of convergence for their system of interest.