Structural breaks occur in timeseries data across a broad range of fields, from economics to nanosciences. For measurements of single-molecule break junctions, structural breaks in conductance versus displacement data occur when the molecular junction ruptures. This moment is significant because the molecule is likely in its most extended geometry, and therefore resembles most closely the geometry used in theoretical predictions. Conventional single-molecule break junction data analysis, on the other hand, typically uses the entire molecular plateau to estimate the single-molecule conductance, which skews the estimate when the plateau is sloped. Borrowing from econometrics, where the study of structural breaks is well established, we present change point detection (CPD) as a tool to search for junction rupture in single-molecule break junction data, and improve estimates in single-molecule conductance. We demonstrate that using CPD instead of the conventional 1D conductance histogram to determine the mean molecular conductance yields a standard deviation in the estimate of typically half that of the conventional approach, greatly improving accuracy. We apply CPD to three separate data sets, two on 4,4'-bipyridine and one on a silane, two at room temperature and one at 4 K, two in one lab, one in another, to show the wide applicability of even the simplest of CPD algorithms: the Chow test. This versatility and better accuracy will propagate into more accurate theoretical simulations. These improved metrics, in turn, will further improve any downstream analyses, including all emerging machine learning approaches.