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      Circular analysis in systems neuroscience: the dangers of double dipping.

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          Abstract

          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.

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          Author and article information

          Journal
          Nat Neurosci
          Nature neuroscience
          Springer Science and Business Media LLC
          1546-1726
          1097-6256
          May 2009
          : 12
          : 5
          Affiliations
          [1 ] Laboratory of Brain and Cognition, US National Institute of Mental Health, Bethesda, Maryland, USA. nikokriegeskorte@gmail.com
          Article
          nn.2303 NIHMS184032
          10.1038/nn.2303
          2841687
          19396166
          f56b4b50-1c5c-4733-9f4f-d4daa2514ca3
          History

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