Quantifying How Much Has Been Learned from a Research Study
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866912545723383808 |
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| author | Mikhaeil, Jonas M. Green, Donald P. |
| author_facet | Mikhaeil, Jonas M. Green, Donald P. |
| contents | How much does a research study contribute to a scientific literature? We propose a learning metric to quantify how much a research community learns from a given study. To do so, we adopt a Bayesian perspective and assess changes in the community's beliefs once updated with a new study's evidence. We recommend the Wasserstein-2 distance as a way to describe how the research community's prior beliefs change to incorporate a study's findings. We illustrate this approach through stylized examples and empirical applications, showing how it differs from more traditional evaluative standards, such as statistical significance. We then extend the framework to the prospective setting, offering a way for decision-makers to evaluate the expected amount of learning from a proposed study. While assessments about what has or could be learned from a research program are often expressed informally, our learning metric provides a principled tool for judging scientific contributions. By formalizing these judgments, our measure has the potential to allow for more transparent assessments of past and prospective research contributions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_14789 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Quantifying How Much Has Been Learned from a Research Study Mikhaeil, Jonas M. Green, Donald P. Methodology Applications How much does a research study contribute to a scientific literature? We propose a learning metric to quantify how much a research community learns from a given study. To do so, we adopt a Bayesian perspective and assess changes in the community's beliefs once updated with a new study's evidence. We recommend the Wasserstein-2 distance as a way to describe how the research community's prior beliefs change to incorporate a study's findings. We illustrate this approach through stylized examples and empirical applications, showing how it differs from more traditional evaluative standards, such as statistical significance. We then extend the framework to the prospective setting, offering a way for decision-makers to evaluate the expected amount of learning from a proposed study. While assessments about what has or could be learned from a research program are often expressed informally, our learning metric provides a principled tool for judging scientific contributions. By formalizing these judgments, our measure has the potential to allow for more transparent assessments of past and prospective research contributions. |
| title | Quantifying How Much Has Been Learned from a Research Study |
| topic | Methodology Applications |
| url | https://arxiv.org/abs/2508.14789 |