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Non-standard errors
(2021)
In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in sample 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: non-standard errors. To study them, we let 164 teams test six hypotheses on the same sample. We find that non-standard errors are sizeable, on par with standard errors. Their size (i) co-varies only weakly with team merits, reproducibility, or peer rating, (ii) declines significantly after peer-feedback, and (iii) is underestimated by participants.
Based on molecular and morphological data of four specimens of Pareas Wagler, 1830 collected from the type locality of P. yunnanensis (Vogt, 1922), along with examination of the type specimens of P. yunnanensis, we revalidate this poorly known, secretive species. Furthermore, based on molecular and morphological lines of evidence we also describe a new species of Pareas from Xishuangbanna Prefecture, Yunnan Province, China. Morphologically, the new species closely resembles its sister species P. nigriceps Guo & Deng, 2009. However, the new species is divergent from the latter in cytochrome b mtDNA gene sequences, and can be distinguished from all congeners by the following combination of morphological characteristics: single preocular, postocular fused with subocular, loreal not bordering orbit, vertebral scales enlarged, 3–5 rows of mid-dorsal scales keeled at the middle of the body, ventral scales 160–171; subcaudals 62–64, dorsal surface of head solid black or reddish-brown, dark nuchal band present, iris brownish-black or reddish-brown.