Interventioncentral.org receives more than a million visits a year (Strait et al., 2015). One of its most commonly used resources-receiving more than 100,000 visits-is the math worksheet generator. On their web page content area, Curriculum-Based Measurement (CBM) Warehouse, interventioncentral.org advertises the math worksheet generator as an online CBM application (Intervention Central, 2017; see http://www.interventioncentral.org/curriculum-basedmeasurement-reading-math-assesment-tests). In general, CBMs are brief and standardized skill-based measures that correspond to an entire curriculum (Deno, 2003; Foegen & Deno, 2001). Given their brevity, CBMs are used to measure students' response to interventions and identify students in need of intervention (Hosp, Hosp, & Howell, 2016; Shapiro, 2011). Recently, Strait and colleagues (2015) evaluated the reliability of randomly generated math-CBMs (M-CBMs) from interventioncentral.org's math worksheet generator. The M-CBMs corresponded with a remedial math intervention curriculum for sixth-grade students with skill deficits in addition, subtraction, multiplication, and division. Strait and colleagues reported that aggregating (averaging) two to three randomly generated M-CBMs created a single reliable (i.e., test-retest reliability) score greater than >.80, whereas averaging four resulted in reliability estimates exceeding .90 (Strait, 2008). Unfortunately, having to give multiple M-CBMs to obtain a single reliable score (i.e., >.80) is inefficient and decreases the measure's functionality as a progress-monitoring tool-challenging the categorization of these assessments as M-CBMs (Strait et al., 2015). However, in this article, given that interventioncentral.org advertises these measures as CBMs and they were designed as brief and standardized measures for a remedial math curriculum, we continue to refer to them as M-CBMs-albeit M-CBMs with questionable technical adequacy. As such, this study attempts to provide more information on the technical adequacy and utility of these M-CBMs to identify students in need of intervention. Currently, it is assumed based on the Spearman-Brown prophecy formula (Strait et al., 2015) that using aggregated M-CBMs would improve predictive validity by improving test-retest reliability, but this assumption is untested.