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For small numbers of trials (<30 per label), determination coefficients can become rather large even if there is no significant effect. To assess significance under the (robust) assumption of Gaussianity, determination coefficients may be converted into t-values according to | For small numbers of trials (<30 per label), determination coefficients can become rather large even if there is no significant effect. To assess significance under the (robust) assumption of Gaussianity, determination coefficients may be converted into t-values according to | ||
<math>t^2=(n-2) frac{r^2}{1-r^2}</math> | <math>t^2=(n-2) \frac{r^2}{1-r^2}</math> | ||
and used in a one-tailed t-test against the "no correlation" null hypothesis. | and used in a one-tailed t-test against the "no correlation" null hypothesis. | ||
[[Category:Glossary]] | [[Category:Glossary]] | ||
Revision as of 17:33, 16 January 2008
A glossary of general terms
(Coefficient of Determination)
The statistical measure calculates the fraction of the total signal variance that is accounted for by the task.
For small numbers of trials (<30 per label), determination coefficients can become rather large even if there is no significant effect. To assess significance under the (robust) assumption of Gaussianity, determination coefficients may be converted into t-values according to
and used in a one-tailed t-test against the "no correlation" null hypothesis.