Bibliographic record
Abstract
The number of methods for evaluating, and possibly making statistical decisions about, null contrasts - or their small sub-set, multiple comparisons - has grown extensively since the early 1950s. That demonstrates how important the subject is, but most of the growth consists of modest variations of the early methods. This paper examines nine fairly basic procedures, six of which are methods designed to evaluate contrasts chosen post hoc, i.e., after an examination of the test data. Three of these use experimentwise or familywise type 1 error rates (Scheffé 1953, Tukey 1953, Newman-Keuls, 1939 and 1952), two use decision-based type 1 error rates (Duncan 1951 and Rodger 1975a) and one (Fisher's LSD 1935) uses a mixture of the two type 1 error rate definitions. The other three methods examined are for evaluating, and possibly deciding about, a limited number of null contrasts that have been chosen independently of the sample data - preferably before the data are collected. One of these (planned t-tests) uses decision-based type 1 error rates and the other two (one based on Bonferroni's Inequality 1936, and the other Dunnett's 1964 Many-One procedure) use a familywise type 1 error rate. The use of these different type 1 error rate definitionsA creates quite large discrepancies in the capacities of the methods to detect true non-zero effects in the contrasts being evaluated. This article describes those discrepancies in power and, especially, how they are exacerbated by increases in the size of an investigation (i.e., an increase in J, the number of samples being examined). It is also true that the capacity of a multiple contrast procedure to 'unpick' 'true' differences from the sample data is influenced by the type of contrast the procedure permits. For example, multiple range procedures (such as that of Newman-Keuls and that of Duncan) permit only comparisons (i.e., two-group differences) and that greatly limits their discriminating capacity (which is not, technically speaking, their power). Many methods (those of Scheffé, Tukey's HSD, Newman-Keuls, Fisher's LSD, Bonferroni and Dunnett) place their emphasis on one particular question, "Are there any differences at all among the groups?" Some other procedures concentrate on individual contrasts (i.e., those of Duncan, Rodger and Planned Contrasts); so are more concerned with how many false null contrasts the method can detect. This results in two basically different definitions of detection capacity. Finally, there is a categorical difference between what post hoc methods and those evaluating pre-planned contrasts can find. The success of the latter depends on how wisely (or honestly well informed) the user has been in planning the limited number of statistically revealing contrasts to test. That can greatly affect the method's discriminating success, but it is often not included in power evaluations. These matters are elaborated upon as they arise in the exposition below. DOI:10.2458/azu_jmmss_v4i1_rodger
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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 | 0.272 | 0.680 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.033 | 0.004 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".