Sample Size and Statistical Reliability
Small program cohorts produce noisy outcomes. Salary based on 12 graduates is not the same as one based on 400. Before citing a number in a report or presentation, check the sample behind it. As a general rule, treat any figure derived from fewer than 30 records with caution, and note the sample size alongside the figure when sharing externally.
Recency vs. Longitudinal Depth
More recent cohorts reflect current labor market conditions, but older cohorts give you longer outcome windows (five-year wages versus one-year wages, for example). For program review, longitudinal depth usually matters more. For enrollment marketing, recency usually matters more. Know which lens your use case requires.
Geography
Labor markets vary significantly by state and region. A median wage figure that looks weak nationally may be competitive for your labor market. Always compare against the appropriate geographic benchmark, not just a national average.
Program Level vs. Institution level data
Institutional aggregates can mask wide variation across programs. An institution-wide employment rate of 87 percent might look strong, but that aggregate could include high-performing programs alongside programs that are significantly below the mean. If you are making program-specific decisions, work at the program level.
Methodology Transparency
If the data will be used in an accreditation self-study, a state report, or any external communication, you should be able to explain where it comes from and how it was produced. Steppingblocks data draws from over 150 million workforce profiles. Be prepared to describe the data sourcing approach to your audience. Contact your Partner Success Associate if you need a methodology summary for your specific use case.