Circular analysis in systems neuroscience: The dangers of double dipping

Nikolaus Kriegeskorte, W. Kyle Simmons, Patrick S. Bellgowan, Chris I. Baker

Research output: Contribution to journalArticlepeer-review

2054 Scopus citations


A neuroscientific experiment typically generates a large amount of data, of which only a small fraction is analyzed in detail and presented in a publication. However, selection among noisy measurements can render circular an otherwise appropriate analysis and invalidate results. Here we argue that systems neuroscience needs to adjust some widespread practices to avoid the circularity that can arise from selection. In particular, 'double dipping', the use of the same dataset for selection and selective analysis, will give distorted descriptive statistics and invalid statistical inference whenever the results statistics are not inherently independent of the selection criteria under the null hypothesis. To demonstrate the problem, we apply widely used analyses to noise data known to not contain the experimental effects in question. Spurious effects can appear in the context of both univariate activation analysis and multivariate pattern-information analysis. We suggest a policy for avoiding circularity.

Original languageEnglish
Pages (from-to)535-540
Number of pages6
JournalNature Neuroscience
Issue number5
StatePublished - May 2009
Externally publishedYes


Dive into the research topics of 'Circular analysis in systems neuroscience: The dangers of double dipping'. Together they form a unique fingerprint.

Cite this