The effect of dependence between observations on the proper interpretation of statistical evidence
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In a recent securities law case, the statistical methods used by the regulator in analysing data on daily commissions and hypothetical profits from initial public offerings (IPOs) assumed that the data on consecutive days were independent. Consecutive observations in most business and economic data, however, are positively correlated. While statistical articles demonstrate that this type of dependence affects the distribution of virtually all statistics, including non-parametric and goodness-of-fit tests, the magnitude of the effect may not be fully appreciated. For example, in one comparison of commissions one broker received on days with an IPO to the days when no IPO was issued yielded a statistically significant p-value of 0.02, under the independence assumption. Accounting for serial correlation, the test actually had a non-significant p-value close to 0.09. Other examples of the effect of dependence include jury discrimination cases in locales where grand jurors can serve two consecutive terms as well as cases concerned with environmental pollution where measurements are spatially and temporally correlated. This paper describes the noticeable effect violations of the independence assumption can have on statistical inferences. The methods for correcting some standard non-parametric tests for serial correlation are also discussed.
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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 | 0.001 | 0.014 |
| 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 | 0.001 |
| 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