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Fitting models to measured data is one of the standard tasks in the naturalsciences, typically addressed early on in physics education in the context oflaboratory courses, in which statistical methods play a central role inanalysing and interpreting experimental results. The increased emphasis placedon such methods in modern school curricula, together with the availability ofpowerful free and open-source software tools geared towards scientific dataanalysis, form an excellent premise for the development of new teachingconcepts for these methods at the university level. In this article, we presentkafe2, a new tool developed at the Faculty of Physics at the KarlsruheInstitute of Technology, which has been used in physics laboratory courses forseveral years. Written in the {\it Python} programming language and makingextensive use of established numerical and optimization libraries, {\it kafe2}provides simple but powerful interfaces for numerically fitting model functionsto data. The tools provided allow for fine-grained control over many aspects ofthe fitting procedure, including the specification of the input data and ofarbitrarily complex model functions, the construction of complex uncertaintymodels, and the visualization of the resulting confidence intervals of themodel parameters.

kafe2 -- a Modern Tool for Model Fitting in Physics Lab Courses