Long run consequence of p-hacking
Fuente:
arXiv
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| Autore principale: | |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866914753760198656 |
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| author | Wang, Xuanye |
| author_facet | Wang, Xuanye |
| contents | We study the theoretical consequence of p-hacking on the accumulation of knowledge under the framework of mis-specified Bayesian learning. A sequence of researchers, in turn, choose projects that generate noisy information in a field. In choosing projects, researchers need to carefully balance as projects generates big information are less likely to succeed. In doing the project, a researcher p-hacks at intensity $\varepsilon$ so that the success probability of a chosen project increases (unduly) by a constant $\varepsilon$. In interpreting previous results, researcher behaves as if there is no p-hacking because the intensity $\varepsilon$ is unknown and presumably small. We show that over-incentivizing information provision leads to the failure of learning as long as $\varepsilon\neq 0$. If the incentives of information provision is properly provided, learning is correct almost surely as long as $\varepsilon$ is small. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_08984 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Long run consequence of p-hacking Wang, Xuanye Theoretical Economics We study the theoretical consequence of p-hacking on the accumulation of knowledge under the framework of mis-specified Bayesian learning. A sequence of researchers, in turn, choose projects that generate noisy information in a field. In choosing projects, researchers need to carefully balance as projects generates big information are less likely to succeed. In doing the project, a researcher p-hacks at intensity $\varepsilon$ so that the success probability of a chosen project increases (unduly) by a constant $\varepsilon$. In interpreting previous results, researcher behaves as if there is no p-hacking because the intensity $\varepsilon$ is unknown and presumably small. We show that over-incentivizing information provision leads to the failure of learning as long as $\varepsilon\neq 0$. If the incentives of information provision is properly provided, learning is correct almost surely as long as $\varepsilon$ is small. |
| title | Long run consequence of p-hacking |
| topic | Theoretical Economics |
| url | https://arxiv.org/abs/2404.08984 |