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Nonstandard Errors
In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty—nonstandard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for more reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants.
Ketersediaan
| Call Number | Location | Available |
|---|---|---|
| PSB lt.2 - Karya Akhir (Koleksi Majalah) | 1 |
| Penerbit | USA: The American Finance Association 2024 |
|---|---|
| Edisi | Volume 79, Issue 3, June 2024, Pages 2339-2390 |
| Subjek | Statistics Data-Generating Process Evidence-Generating Process Nonstandard Errors |
| ISBN/ISSN | 1540-6261 |
| Klasifikasi | NONE |
| Deskripsi Fisik | ill, chart, table, grafik, 678 hal, 20 cm |
| Info Detail Spesifik | The Journal of Finance |
| Other Version/Related | Tidak tersedia versi lain |
| Lampiran Berkas |