Reliable Enough: What Evaluators Should Know About Measurement Error
This fall, as part of my doctoral coursework in measurement, I’ve been immersing myself in some of the theories and frameworks we use to measure complex human constructs. But don’t go just yet — I’m not going to write a lecture on the foundations of Classical Test Theory (CTT) or Factor Analysis. I’ll save those for my continued suffering as I navigate this course.
What I want to share instead is one of my biggest takeaways so far: the realization that the numbers we often rely on in evaluation are not as exact as they seem.
In evaluation, we often treat numbers as certainties, a survey score, a rubric rating, a test result. But beneath every number lies a quiet reality: no data are ever perfect. Every measure we use carries a margin of uncertainty, shaped by human judgment, context, and the limits of our instruments.
This is where measurement error enters the picture, though not in the way most people think.
In psychometrics (the science of how we measure what can’t be seen directly), error doesn’t mean a mistake. Here, error is a more abstract concept that refers to the difference between what we can capture through a questionnaire and what the real answer is. Technically, error is the noise, those unpredictable factors that slightly distort our picture of reality. Practically, it’s everything that influences how a participant responds to a questionnaire.
How this Noise Shows Up in Evaluation and Education
In education and program evaluation, measurement error quietly shapes much of what we do. It shows up when teacher observation scores differ depending on who the observer is. It appears when survey responses change based on wording or timing. It even affects standardized test results where a student’s score might fluctuate from one day to another, not because they suddenly learned or forgot content, but because of ordinary variation in attention, mood, or testing conditions.
In all these cases, the data are not wrong, they’re simply imperfect reflections of something real. Understanding that helps evaluators interpret results more responsibly.
Why “Reliable Enough” Is a Realistic Goal
It’s easy to think that every measure in evaluation should be completely reliable, that scores should stay the same no matter what. But in the real world, we’re dealing with people, not machines. Perfect reliability doesn’t exist.
That’s why the goal is to be reliable enough for the purpose of the decision.
For accountability or high-stakes decisions, we want instruments that are highly consistent and tested.
For program improvement or formative evaluation, a moderate level of reliability may still offer useful, trustworthy information.
The key is transparency, knowing the limitations of our measures and being honest about what the numbers can and cannot tell us.
Reducing Error in Practical Ways
While we can’t eliminate measurement error, we can reduce it. Here are a few practical strategies that evaluators and educators can use:
Use multiple measures. A single test or survey rarely tells the whole story. Combining sources (e.g., observations, student work, perception data) helps balance out random error.
Train raters and observers. Clear criteria, calibration sessions, and examples reduce differences in how people interpret rubrics or performance standards.
Pilot and review instruments. Even short surveys benefit from a test run to spot confusing wording or inconsistent interpretations. Even a small group of peers in the office can help you improve the instrument; never underestimate the value of peer reviews!
Control conditions where possible. Small details like timing, instructions, and environment can make data more consistent.
Communicate uncertainty. Share results as ranges or trends, not single point “truths.” This models data literacy and builds trust.
Closing Reflection: Measurement Humility
Acknowledging measurement error doesn’t make our data weaker, it makes our interpretation wiser. Reliable enough doesn’t mean careless; it means realistic, reflective, and transparent about the limits of what we measure.
As evaluators, our credibility comes not from claiming precision we don’t have, but from showing we understand the uncertainty that lives inside every number. Because in the end, it’s not about chasing perfect data, it’s about using imperfect data responsibly.
