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Related Concept Videos

Correlations02:20

Correlations

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Correlation means that there is a relationship between two or more variables (such as ice cream consumption and crime), but this relationship does not necessarily imply cause and effect. When two variables are correlated, it simply means that as one variable changes, so does the other. We can measure correlation by calculating a statistic known as a correlation coefficient. A correlation coefficient is a number from -1 to +1 that indicates the strength and direction of the relationship between...
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Cause and Effect01:53

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While variables are sometimes correlated because one does cause the other, it could also be that some other factor, a confounding variable, is actually causing the systematic movement in our variables of interest. For instance, as sales in ice cream increase, so does the overall rate of crime. Is it possible that indulging in your favorite flavor of ice cream could send you on a crime spree? Or, after committing crime do you think you might decide to treat yourself to a cone?
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Correspondence bias, also referred to as the fundamental attribution error, describes the tendency to attribute another person’s behavior to internal characteristics rather than situational influences. This cognitive bias leads individuals to overlook external factors that may be influencing actions, thereby fostering potentially inaccurate assessments of others’ intentions and dispositions.Empirical Evidence for Correspondence BiasResearch has consistently demonstrated the...
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Correlation01:09

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In statistics, two variables are said to be correlated if the values of one variable are associated with the other variable. Depending on the relationship between two variables, correlation can be of three types– positive correlation, negative correlation, and zero correlation.
Two variables, for example, a and b, are said to be positively correlated if both variables move in the same direction. In other words, a positive correlation exists between two variables, a and b, if:
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Correlation and Causation01:27

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Statistical tests can calculate whether there is a relationship, or correlation, between independent and dependent variables. An indirect relationship of the variables signifies a correlation, while a direct relationship shows causation. If it is determined that no connection exists between the variables, then the correlation is a coincidence.
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Spearman's Rank Correlation Test01:20

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Spearman's rank correlation test, also known as Spearman's rho, is a nonparametric method for assessing the strength and direction of association between two variables. This test is particularly valuable when the data distribution is unknown or when the assumption of normality does not hold. Named after the English psychologist and statistician Dr. Charles Edward Spearman, it serves as the nonparametric counterpart to Pearson's correlation coefficient.
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Studying Metabolic Brain Connectivity Using 2-Deoxy-2-[18F]Fluoro-D-Glucose Dynamic Positron Emission Tomography at the Single-subject Level
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Selective correlations; not voodoo.

J D Rosenblatt1, Y Benjamini2

  • 1Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel.

Neuroimage
|August 26, 2014
PubMed
Summary
This summary is machine-generated.

Neuroimaging studies face "voodoo" correlations from selective inference. This new method creates confidence intervals (CIs) controlling the False Coverage Rate (FCR) without discarding data, offering better reproducibility.

Keywords:
CorrelationsEffect sizeSelection biasSocial neuroscienceVoodoo

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Area of Science:

  • Neuroimaging
  • Statistical Neuroscience
  • Brain Imaging Analysis

Background:

  • "Voodoo" correlations, or exceptionally high correlations in selected brain regions, are a known issue in neuroimaging.
  • This problem arises from estimating quantities of interest from the same data used for selection, a statistical challenge known as selective inference.
  • Current remedies like data splitting have drawbacks, including discarding valuable data.

Purpose of the Study:

  • To develop a novel statistical method for constructing confidence intervals (CIs) that address selective inference in neuroimaging.
  • To ensure good reproducibility prospects by controlling the False Coverage Rate (FCR) even when selection and estimation use the same data.
  • To provide a more informative and powerful alternative to data-splitting methods.

Main Methods:

  • Adaptation of recent developments in selective inference to create new confidence intervals.
  • Control of the expected proportion of non-covered correlations in selected voxels (False Coverage Rate - FCR).
  • Development of a "confidence calibration plot" for clear and interpretable reporting of results.

Main Results:

  • Proposed confidence intervals control the FCR in realistic social neuroscience simulations.
  • The selective intervals attenuate the impression of highly biased observed correlations by extending towards zero.
  • The method demonstrated considerable selection bias in a loss-aversion study, highlighting the need for such corrections.
  • Selective intervals showed more power and were more informative than data-splitting, as no data was discarded.

Conclusions:

  • The proposed selective inference method provides reliable confidence intervals in neuroimaging, mitigating "voodoo" correlations.
  • This approach offers improved statistical rigor and reproducibility compared to traditional data-splitting techniques.
  • The accompanying software package facilitates the computation and application of these advanced statistical intervals in neuroimaging research.