Bibliographic record
Abstract
Experimentation is a vital tool in innovation, entrepreneurship and business in general. We observe that the value of experimentation often lies in the contrast between the anticipated value of the top result among two or more trials and the expected value of a single attempt. This concept aligns with the statistical theory of the maximum of multiple draws from a probability distribution, known as the last order statistic. The implications of this alignment between experimentation and order statistics have not been fully appreciated or elaborated on in the management literature. We demonstrate that when the probability distribution of potential outcomes is known, the properties of the last-order statistic can provide us with a precise imputation of value to experimentation such that values can be calculated for the advantage of conducting multiple experiments over one. This advantage comes in the form of increase in central tendency (mean or median) and change in dispersion (variance or standard deviation), as well as increased likelihood of outliers. We calculate a number of measures of the advantage of pure experimentation for two common types of probability distribution commonly observed in business outcomes: the Normal Distribution and the Pareto (Power Law) Distribution. We find interesting insights including a “golden rule” of variance reduction that applies to any normal distribution regardless of its parameters.
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 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 | 0.313 | 0.658 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.020 |
| Scholarly communication | 0.011 | 0.019 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".