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

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

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

A Transdisciplinary Approach To Business Education Throughout Family Firms As Communities Of Practice

AbstractThe paper explores the relationship between family businesses (FB), communities of practice (CoP), and entrepreneurship education in the context of the knowledge production and sharing within and between these communities. The relationship between these three spheres of knowledge is presented from a transdisciplinary point of view. The contextual legitimacy identifies the communicational channels between the fields of family businesses and the communities of practice and entrepreneurial education in a transdisciplinary generative synergistic context. The notion of CoP suggests that organizational community boundaries do not correspond with typical functional boundaries, including practice - and person - based networks, while family businesses underlie the necessity of sustainable business education which can be achieved through collaborative interaction in a creative entrepreneurial framework. The paper proposes to find the common ground of the three fields of knowledge and the way these points develop new knowledge windows towards a new entrepreneurial understanding of life, and generating new alternatives.</jats:p