The Comedy of Measurement Errors: Standard Error of Measurement and Standard Error of Estimation
Bibliographic record
Abstract
Testing is used to inform a range of critical decisions that help structure much of contemporary society. An unavoidable aspect of testing is that test scores are not infallible. As a result, individual test scores should be accompanied by an interval that indicates the uncertainty surrounding the score. There are a number of different test-score intervals that can be created from different error terms. Unfortunately, there are pervasive misinterpretations of these errors and their intervals. Many of these interpretations can be found in authoritative sources on psychological measurement, which has resulted in stubborn and persistent confusion about what these intervals mean. In the current article, we clarify two important error terms and their intervals: (a) the Standard Error of Estimation and (b) the Standard Error of Measurement. We explicate the meaning and interpretation of these errors by examining their statistical foundations. Specifically, we detail how these terms are formulated from different statistical models and the implications of these models for their different interpretations. We use classical test theory, bivariate linear regression, R activities, and algebra to illustrate the key concepts and differences.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.195 | 0.570 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.002 | 0.027 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".