Analysis of the Effect of Multiple Testing in Assessing Tobacco Product Differences
Bibliographic record
Abstract
Summary During the last two decades, an increase of tobacco product reporting requirements from regulators was observed, such as Europe, Canada or USA. However, the capacity to compare and discriminate accurately two products is impacted by the number of constituents used for the comparison. Indeed, performing a large number of simultaneous independent hypothesis tests increases the probability of rejection of the null hypothesis when it should not be rejected. This leads to virtually guarantee the presence of type I errors among the findings. Correction methods have been developed to overcome this issue like the Bonferroni or Benjamini & Hochberg ones. The performance of these methods was assessed by comparing identical tobacco products with different sizes of data sets. Results showed that multiple comparisons lead to erroneous conclusions if the risk of type I error is not corrected. Unfortunately, reducing the type I error impacts the statistical power of the tests. Consequently, strategies for dealing with multiplicity of data should provide a reasonable balance between testing requirement and statistical power of differentiation. Multiple testing for product comparison is less of a problem if studies restrict to the most relevant parameters for comparison.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.004 | 0.328 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".