Circular analysis

<h2 id="examples">Examples</h2>
<p>At its most simple, it can include the decision to remove outliers, after noticing this might help improve the analysis of an experiment. The effect can be more subtle. In <a href="/facts/Functional_magnetic_resonance_imaging/GIayRTAL">functional magnetic resonance imaging</a> (fMRI) data, for example, considerable amounts of pre-processing is often needed. These might be applied incrementally until the analysis 'works'. Similarly, the classifiers used in a <a href="/facts/Multivoxel_pattern_analysis/GIayRTAL">multivoxel pattern analysis</a> of fMRI data require parameters, which could be tuned to maximise the classification accuracy.
</p><p>In geology, the potential for circular analysis has been noted<a class="footnote-ref" id="fnref:1" href="#fn:1"><sup>1</sup></a> in the case of maps of geological faults, where these may be drawn on the basis of an assumption that faults develop and propagate in a particular way, with those maps being later used as evidence that faults do actually develop in that way.
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<h2 id="solutions">Solutions</h2>
<p>Careful design of the analysis one plans to perform, prior to collecting the data, means the analysis choice is not affected by the data collected. Alternatively, one might decide to perfect the classification on one or two participants, and then use the analysis on the remaining participant data. Regarding the selection of classification parameters, a common method is to divide the data into two sets, and find the optimum parameter using one set and then test using this parameter value on the second set. This is a standard technique used (for example) by the princeton MVPA classification library.<a class="footnote-ref" id="fnref:2" href="#fn:2"><sup>2</sup></a>
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<h2 id="notes">Notes</h2>

<ul><li>Kriegeskorte, N.; Simmons, W. K.; Bellgowan, P. S. F.; Baker, C. I. (2009). <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2841687">"Circular analysis in systems neuroscience: The dangers of double dipping"</a>. <i>Nature Neuroscience</i>. 12 (5): 535–540. <a href="/facts/Doi_(identifier)/muM9Etpq">doi</a>:<a href="https://doi.org/10.1038%2Fnn.2303">10.1038/nn.2303</a>. <a href="/facts/PMC_(identifier)/dX1zMt71">PMC</a> <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2841687">2841687</a>. <a href="/facts/PMID_(identifier)/JlHAvMHt">PMID</a> <a href="https://pubmed.ncbi.nlm.nih.gov/19396166">19396166</a>.</li>
<li>Kriegeskorte, N.; Lindquist, M. A.; Nichols, T. E.; Poldrack, R. A.; Vul, E. (2010). <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2949251">"Everything you never wanted to know about circular analysis, but were afraid to ask"</a>. <i>Journal of Cerebral Blood Flow & Metabolism</i>. 30 (9): 1551–1557. <a href="/facts/Doi_(identifier)/muM9Etpq">doi</a>:<a href="https://doi.org/10.1038%2Fjcbfm.2010.86">10.1038/jcbfm.2010.86</a>. <a href="/facts/PMC_(identifier)/dX1zMt71">PMC</a> <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2949251">2949251</a>. <a href="/facts/PMID_(identifier)/JlHAvMHt">PMID</a> <a href="https://pubmed.ncbi.nlm.nih.gov/20571517">20571517</a>.</li>
<li>Tolstrup, N.; Rouzé, P.; Brunak, S. (1997). <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC146848">"A branch point consensus from Arabidopsis found by non-circular analysis allows for better prediction of acceptor sites"</a>. <i>Nucleic Acids Research</i>. 25 (15): 3159–3163. <a href="/facts/Doi_(identifier)/muM9Etpq">doi</a>:<a href="https://doi.org/10.1093%2Fnar%2F25.15.3159">10.1093/nar/25.15.3159</a>. <a href="/facts/PMC_(identifier)/dX1zMt71">PMC</a> <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC146848">146848</a>. <a href="/facts/PMID_(identifier)/JlHAvMHt">PMID</a> <a href="https://pubmed.ncbi.nlm.nih.gov/9224618">9224618</a>.</li>
<li>Olivetti, E.; Mognon, A.; Greiner, S.; Avesani, P. (2010). "Brain Decoding: Biases in Error Estimation". <i>2010 First Workshop on Brain Decoding: Pattern Recognition Challenges in Neuroimaging</i>. pp. 40–43. <a href="/facts/Doi_(identifier)/muM9Etpq">doi</a>:<a href="https://doi.org/10.1109%2FWBD.2010.9">10.1109/WBD.2010.9</a>. <a href="/facts/ISBN_(identifier)/15AdSPa9">ISBN</a> 978-1-4244-8486-7. <a href="/facts/S2CID_(identifier)/ldJsHa2Y">S2CID</a> <a href="https://api.semanticscholar.org/CorpusID:13024947">13024947</a>.</li></ul>

<h2 id="references">References</h2>

<ol>
<li id="fn:1"><p>Scott, D. L.; Braun, J.; Etheridge, M. A. (1994). "Dip analysis as a tool for estimating regional kinematics in extensional terranes". Journal of Structural Geology. 16 (3): 393. doi:10.1016/0191-8141(94)90043-4. <a href="/wiki/Doi_(identifier)" target="_blank">/wiki/Doi_(identifier)</a> <a href="#fnref:1" class="footnote-back-ref">↩</a></p></li>
<li id="fn:2"><p>"Princeton Multi-Voxel Pattern Analysis (MVPA) Toolbox | Neuroscience". pni.princeton.edu. Retrieved 2019-07-23. <a href="https://pni.princeton.edu/pni-software-tools/mvpa-toolbox" target="_blank">https://pni.princeton.edu/pni-software-tools/mvpa-toolbox</a> <a href="#fnref:2" class="footnote-back-ref">↩</a></p></li>
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Circular analysis open-in-new

Circular analysis