title: Everything you never wanted to know about circular analysis, but were afraid to ask.
abstract: Over the past year, a heated discussion about 'circular' or 'nonindependent' analysis in brain imaging has emerged in the literature. An analysis is circular (or nonindependent) if it is based on data that were selected for showing the effect of interest or a related effect. The authors of this paper are researchers who have contributed to the discussion and span a range of viewpoints. To clarify points of agreement and disagreement in the community, we collaboratively assembled a series of questions on circularity herein, to which we provide our individual current answers in <or=100 words per question. Although divergent views remain on some of the questions, there is also a substantial convergence of opinion, which we have summarized in a consensus box. The box provides the best current answers that the five authors could agree upon.
authors.affiliation: MRC Cognition and Brain Sciences Unit, Cambridge, UK.
authors.forename: Nikolaus
authors.initials: N
authors.lastname: Kriegeskorte
authors.forename: Martin A
authors.initials: MA
authors.lastname: Lindquist
authors.forename: Thomas E
authors.initials: TE
authors.lastname: Nichols
authors.forename: Russell A
authors.initials: RA
authors.lastname: Poldrack
authors.forename: Edward
authors.initials: E
authors.lastname: Vul
mesh_terms: D001921:Brain / Q000033:anatomy & histology* / Q000473:pathology; D001931:Brain Mapping / Q000379:methods; D003627:Data Interpretation, Statistical; D017408:Guidelines as Topic; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D009488:Neurosciences / Q000379:methods* / Q000706:statistics & numerical data; D011642:Publications; D012107:Research Design; D015983:Selection Bias