Curriculum-based measurement of oral reading (CBM-R) progress monitoring data is used to measure student response to instruction. Federal legislation permits educators to use CBM-R progress monitoring data as a basis for determining the presence of specific learning disabilities. However, decision making frameworks originally developed for CBM-R progress monitoring data were not intended for such high stakes assessments. Numerous documented issues with trend line estimation undermine the validity of using slope estimates to infer progress. One proposed recommendation is to use confidence interval overlap as a means of judging reliable growth. This project explored the degree to which confidence interval overlap was related to true growth magnitude using simulation methodology. True and observed CBM-R scores were generated across 7 durations of data collection (range 6--18 weeks), 3 levels of dataset quality or residual variance (5, 10, and 15 words read correct per minute) and 2 types of data collection schedules. Descriptive and inferential analyses were conducted to explore interactions between overlap status, progress monitoring scenarios, and true growth magnitude. A small but statistically significant interaction was observed between overlap status, duration, and dataset quality, b = -0.004, t(20992) =-7.96, p < 0.001. In general, confidence interval overlap does not appear to meaningfully account for variance in true growth across many progress monitoring conditions. Implications for research and practice are discussed. Limitations and directions for future research are addressed.