Long-range interactions and probability distribution functions in the interplanetary medium Pronounced core-halo electron and ion velocity distribution characteristics are a persistent and ubiquitous feature of astrophysical plasmas, in particular manifest in the solar wind by space observations. On the other hand, the probability distributions (PDFs) of the differences of any physical variable in the intermittent, turbulent interplanetary medium are scale dependent and also subject to strong non-Gaussianity for small-scale spatial separations, whereas for large scales the differences turn into a Gaussian normal distribution. Theoretically the nonextensive character of the interplanetary medium, counting for long-range interactions or memory and non-locality in turbulence, can be introduced by pseudo-additive entropy generalization where a parameter kappa measures the degree of nonextensivity in the system. Compatible also with negative values of the parameter kappa, generalized thermo-statistics provides via a recently introduced single temperature bi-kappa function the observed pronounced core-halo solar wind velocity space patterns that are generated adiabatically and following density conservation out of the infinite kappa Maxwellian state. Similarly, the PDFs of solar wind scalar field differences, computed from WIND and ACE data for different time lags, are compared with the characteristics of the theoretical bi-kappa functional and found also to represent accurately the overall scale dependence of the spatial solar wind intermittency. The observed PDF characteristics for increased spatial scales are manifest in the theoretical distribution functional by enhancing the only tuning parameter kappa where the large-scale Gaussian is approached for infinite kappa. It is argued that non-thermal core-halo velocity space distributions as well as the intermittency of the turbulent fluctuations must be related physically to the non-extensive character of the interplanetary medium counting for the long-range interactions via the entropy generalization.