Non-Gaussian distributions under scrutiny

TL;DR

This paper emphasizes the importance of careful interpretation of non-Gaussian data, particularly cautioning against over-reliance on q-Gaussian models without thorough validation.

Contribution

It provides a critical commentary on the misuse of q-Gaussian distributions in modeling non-Gaussian data, highlighting potential pitfalls.

Findings

Warns against misinterpreting non-Gaussian data as q-Gaussians

Highlights the need for careful statistical analysis

Calls for rigorous validation of distribution assumptions

Abstract

Comment of the very interesting paper by Hilhorst & Schehr, J. Stat. Mech. P06003 (2007). The main point is that one should be extremely careful when interpreting non-Gaussian data in terms of q-Gaussians.