Questions and Answers
The Maimon Research distance education program has received, in a short while, considerable interest in how the key concepts of measurement and falsification can be transmitted to colleagues and students Understandably, there is considerable pushback. As interactive education is a key element is escaping from a mindset that denies measurement standards and evaluable claims, this has resulted in a correspondence that looks to set to grow exponentially.
To mitigate the burden of responding to inquiries, as we should, we have established a QUESTION AND ANSWERS section on the Maimon Research website (www.maimonresearch,com) to support ‘generic’ response. Please feel free to send your questions and comments to Langleylapaloma@gmail.com.
Let us know if this works for you. Click on the question for the answer:
QUESTION 2: Why are interval and ratio scales so essential?
ANSWER 1:
ANSWER 1: The most difficult message to convey in academic life is that an entire area of instruction has rested on foundations that no longer withstand scrutiny. Yet that is precisely the position we now face with health technology assessment. For more than two decades, graduate programs across universities, not just ours, have taught a form of HTA that assumes its evaluative machinery—utility scores, QALYs, ordinal preference elicitation, and simulation modelling—constitutes a scientific framework. That assumption has been comfortable, convenient, and institutionally reinforced, but it has also been mistaken. The problem is not a matter of interpretation or philosophy. It is a matter of measurement. The field built an edifice of quantitative claims without first establishing whether the numbers it used possessed the properties required to support arithmetic operations. In consequence, HTA has been teaching students how to manipulate quantities that are not actually quantities.
The central failing is the neglect of representational measurement theory. None of the instruments routinely used in HTA, time trade-off utilities, standard gamble values, multiattribute utility scores, meet the axioms required for interval or ratio measurement. The scores are ordinal at best. Multiplying an ordinal preference value by time does not transform it into a quantity, yet this multiplication is the defining operation behind the QALY. The reference case model that is taught as the gold standard uses these non-measures as inputs and generates elaborate numerical outputs. The sophistication of the modelling has concealed a conceptual flaw: the numbers have no lawful arithmetic structure, so the outputs, however precise they appear, cannot sustain scientific claims. A discipline that mistakes numerical representation for measurement trains students to trust models that cannot be falsified, validated, or interpreted within the framework of normal science.
This is not a local problem. It is a systemic international failure that has persisted because successive generations of practitioners were never educated in measurement theory. They inherited tools, not principles. Graduate programs have understandably perpetuated what appeared to be current best practice, and academic incentives rewarded conformity rather than foundational critique. What has changed is not the underlying theory but our willingness to confront it. The last forty years of HTA are now recognisably an anomaly: a period during which the field operated outside the standards that govern all scientific disciplines.
Explaining this to students and to the wider academic community is not an admission of institutional fault but an act of academic responsibility. Correcting course is not optional. If our program claims to train students in scientific evaluation of health technologies, then it must teach them what scientific evaluation actually requires: unidimensional constructs, interval or ratio measurement where arithmetic is intended, and explicit protocols that produce empirically testable value claims. Anything less perpetuates error.
The goal is not to disown our history but to ensure that our instruction satisfies the same standards of rigor we expect from every other scientific field. If we do not address this gap now, our graduates will continue to practice a methodology that the scientific community will eventually judge as untenable. A curriculum grounded in measurement is not a radical shift. It is simply bringing HTA back into the domain of science.
ANSWER 2:
ANSWER 2: Interval and ratio scales are essential because they are the only kinds of numerical representations that allow the arithmetic operations we routinely rely on to make scientific claims. Science is built on the idea that numbers correspond to differences or ratios in the world that behave lawfully. If the numbers do not have those properties, then the arithmetic we perform on them is meaningless, however familiar or comfortable it feels.
An interval scale ensures that equal numerical differences represent equal differences in the attribute being measured across the entire range. Temperature in Celsius is the standard example: the gap between 20° and 30° is the same magnitude as the gap between 80° and 90°. If a scale does not have constant intervals, subtraction and addition lose their interpretability. You cannot quantify change if the size of a “unit” varies depending on where you are on the scale. Most psychological and health-related scores suffer from exactly this problem: the person who moves from a score of 20 to 40 may not have changed by the same amount as someone who moves from 50 to 70. Treating such scores as if they support arithmetic confuses ranking with measurement.
A ratio scale adds a further requirement: a true zero that represents the absence of the attribute being measured. This gives the scale meaningful ratios. If you say one patient experiences twice as many seizures as another, or that one drug reduces hospital days by 50%, you are implicitly assuming a ratio scale. Multiplication and division only make sense when zero is absolute and intervals are constant. Most physical measurements—length, mass, time—are ratio scales because the mathematics requires it. In HTA, any claim that involves relative change, proportional effects, cost per unit of effect, or efficiency demands ratios. If the dependent variable is not on a ratio scale, the entire analytic structure collapses.
Without interval or ratio properties, numerical scores are simply ordered labels. They can tell you that one state is better than another, but they cannot tell you how much better or whether differences are comparable across the scale. Yet HTA routinely treats ordinal utilities as if they were interval, and then performs ratio operations on them. That is why the QALY is impossible in principle: it requires multiplying time by a value that lacks interval structure and lacks a zero that represents the absence of health. The result is not a measure but a number masquerading as one.
Fundamentally, interval and ratio scales matter because they are the only foundations that allow falsifiable, testable, and comparable claims. Without them, HTA cannot produce evaluable evidence. It can only produce numerical storytelling.