An introduction to an old acquaintance: using Bayesian inference in sales research
Bibliographic record
Abstract
Given the scant attention paid to Bayesian inference in the academic sales literature, researchers could be forgiven for believing that frequentist methods provide the only feasible way for sales researchers to derive important insights for both theory and practice. The purpose of this research is to demonstrate that this belief overlooks the considerable value that Bayesian inference can provide to sales theory and practice. In so doing, we outline fundamental differences between Bayesian and frequentist methods, and describe how these differences can lead to different empirical insights. We review the extant literature that employs Bayesian methods, with an emphasis on how these studies provide insight that may elude frequentist methods. Then, using a sample of 146 B2B salespeople, we empirically demonstrate that the use of Bayesian methods is both within the methodological reach of the vast majority of sales researchers, and can also provide different empirical insights using the same dataset, than would frequentist methods. We then provide some future research ideas to encourage sales researchers to employ Bayesian methods in their own research. Finally, in hopes that readers do not view Bayesian inference as a “silver bullet”, we examine some drawbacks and limitations of this intriguing method of statistical inference.
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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 | 0.018 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.008 | 0.016 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.007 | 0.015 |
| Insufficient payload (model declined to judge) | 0.013 | 0.006 |
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".