In an authorship verification problem one is given writing examples from an author A, and one is asked to determine whether or not each text in fact was written by A. In a more general form of the authorship verification problem one is given a single document d only, and the question is whether or not d contains sections from other authors. The heart of authorship verification is the quantization of an author's writing style along with an outlier analysis to identify anomalies. Human readers are well-versed in detecting such spurious sections since they combine a highly-developed sense for wording with context-dependent meta knowledge in their analysis.The intention of this paper is to compile an overview of the algorithmic building blocks for authorship verification. In particular, we introduce authorship verification problems as decision problems, discuss possibilities for the use of meta knowledge, and apply meta analysis to post-process unreliable style analysis results. Our meta analysis combines a confidence-based majority decision with the unmasking approach of Koppel and Schler. With this strategy we can improve the analysis quality in our experiments by 33% in terms of the F-measure.