Non-Standard 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: Non-standard 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 better reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants

Three Essays on Robust Inference in Economics and Finance

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The rare-earth elements distribution in the Bazhenov shales

The rare-earth elements and yttrium (REY) distribution data with depth in the Bazhenov formation are given for the case of the one well in the Nizhnevartovsk arch of the Western Siberia, Russian Federation. According to the correlation analysis, it was found that REY (mainly LREE) is concentrated in apatite more than in clays or plagioclase, while HREE is preferably accumulated in clay minerals. It was estimated that the water extracts from the Bazhenov formation contain REY up to 0.014 ppb, while LREE is contained in 3.6 times more than HREE. An attempt to estimate the REY content in the pore water of the Bazhenov formation was made using water extract composition data

Reliable Estimates of Interpretable Cue Effects with Active Learning in Psycholinguistic Research

Studying the relative weighting of different cues for the interpretation of a linguistic phenomenon is a core element in psycholinguistic research. This research needs to strike a balance between two things: generalisability to diverse lexical settings, which requires a high number of different lexicalisations and the investigation of a large number of different cues, which requires a high number of different test conditions. Optimizing both is impossible with classical psycholinguistic designs as this would leave the participants with too many experimental trials. Previously we showed that Active Learning (AL) systems allow to test numerous conditions (eight) and items (32) within the same experiment. As stimulus selection was informed by the system’s learning mechanism, AL sped-up the labelling process. In the present study, we extend the use case to an experiment with 16 conditions, manipulated through four binary factors (the experimental setting and three prosodic cues; two levels each). Our findings show that the AL system correctly predicted the intended result pattern after twelve trials only. Hence, AL further confirmed previous findings and proved to be an efficient tool, which offers a promising solution to complex study designs in psycholinguistic research.publishe

A database of water chemistry in eastern Siberian rivers

Measurement(s) water chemistry Technology Type(s) chromatography and spectrometry Sample Characteristic - Environment hydrochemistry Sample Characteristic - Location Eastern Siberi