People spontaneously divide everyday experience into smaller units (event segmentation). To measure event segmentation, studies typically ask participants to explicitly mark the boundaries between events as they watch a movie (segmentation task). Their data may then be used to infer how others are likely to segment the same movie. However, significant variability in performance across individuals could undermine the ability to generalize across groups, especially as more research moves online. To address this concern, we used several widely employed and novel measures to quantify segmentation agreement across different sized groups (n = 2-32) using data collected on different platforms and movie types (in-lab & commercial film vs. online & everyday activities). All measures captured nonrandom and video-specific boundaries, but with notable between-sample variability. Samples of 6-18 participants were required to reliably detect video-driven segmentation behavior within a single sample. As sample size increased, agreement values improved and eventually stabilized at comparable sample sizes for in-lab & commercial film data and online & everyday activities data. Stabilization occurred at smaller sample sizes when measures reflected (1) agreement between two groups versus agreement between an individual and group, and (2) boundary identification between small (fine-grained) rather than large (coarse-grained) events. These analyses inform the tailoring of sample sizes based on the comparison of interest, materials, and data collection platform. In addition to demonstrating the reliability of online and in-lab segmentation performance at moderate sample sizes, this study supports the use of segmentation data to infer when events are likely to be segmented.