Classification based hypothesis testing in neuroscience: Below-chance level classification rates and overlooked statistical properties of linear parametric classifiers.

@article{Jamalabadi2016ClassificationBH,
  title={Classification based hypothesis testing in neuroscience: Below-chance level classification rates and overlooked statistical properties of linear parametric classifiers.},
  author={Hamidreza Jamalabadi and Sarah Alizadeh and Monika Sch{\"o}nauer and Christian Leibold and Steffen Gais},
  journal={Human brain mapping},
  year={2016},
  volume={37 5},
  pages={
          1842-55
        }
}
Multivariate pattern analysis (MVPA) has recently become a popular tool for data analysis. Often, classification accuracy as quantified by correct classification rate (CCR) is used to illustrate the size of the effect under investigation. However, we show that in low sample size (LSS), low effect size (LES) data, which is typical in neuroscience, the distribution of CCRs from cross-validation of linear MVPA is asymmetric and can show classification rates considerably below what would be… CONTINUE READING
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