A New Look at the Influence of Guessing on the Reliability of Multiple-Choice Tests
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Bibliographic record
Abstract
Previous studies have established that chance success due to guessing contributes to error variance and diminishes the reliability of multiple-choice tests and true-false tests. However, the practical usefulness of these theoretical results remains doubtful. Equations that have been derived have not often been used in practical work in testing and test construction. One reason is that relatively little is known about how guessing combines with other sources of error variance that determine test reliability and what proportion of the total variance of test scores is accounted for by guessing. This article derives explicit formulas that allow for combinations of error variance due to guessing and other sources of error. These formulas provide a more realistic guide as to how much improvement in reliability can be expected by altering parameters such as number of test items, number of item choices, and the means and variances of examinees' observed scores.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it