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

Podatność na recykling jako funkcja różnorodności materiałów, z których zbudowana jest maszyna

Modern farming machines are manufactured from a continuously growing number of different materials. This is quite understandable because different parts and units of farming machines work in different conditions, at constantly changing loads and the application of appropriate material guarantee their optimal durability. This direction of development causes that these modern machines, once they have reached the end of their service life, are increasingly more difficult to recycle. Therefore, one of the major tasks of manufacturers is to reconcile these contradictory requirements. This can be achieved by developing a method which will allow to determine precisely, numerically the impact of the material heterogeneity of the farming machines on the recyclability. This study presents such a method which employs the information entropy of material heterogeneity of farming machines as a measure of recyclability.Nowoczesne maszyny rolnicze są budowane z coraz większej liczby różnych materiałów. Jest to oczywiste, ponieważ części i zespoły maszyn rolniczych pracują w różnych warunkach, przy zmiennych obciążeniach, a odpowiednie materiały zapewniają im optymalną trwałość. Taki kierunek rozwoju sprawia, że maszyny te trudniej, po wyłączeniu z eksploatacji zagospodarować na drodze recyklingu. Zadaniem producentów maszyn rolniczych jest umiejętne pogodzenie tych sprzecznych wymagań. Można tego dokonać dysponując metodą, która pozwoli precyzyjnie, liczbowo określić wpływ różnorodności materiałowej maszyn rolniczych na ich podatność na recykling. W pracy przedstawiono taką metodę, która jako miarę podatności na recykling wykorzystuje entropię informacyjną różnorodności materiałowej maszyny rolniczej

Farm building with a positve energy balance

Przeprowadzono badania modeli dachu energetycznego budynku inwentarskiego w okresie od marca do maja 2004. Przedstawione wyniki badań wstępnych potwierdzają celowość zastosowania ogniw fototermicznych w budynkach generujących energię ponad własne zapotrzebowanie.The research was conducted regarding the energetic roof models of a farm building from March to May 2004. The presented results of preliminary research conform the usefulness of applying photothermal cells in buildings generating energy in excess of their own needs

Non-Standard Errors

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

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

Non-Standard Errors

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