# statistical independence

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## statistical independence

[stə′tis·tə·kəl ‚in·də′pen·dəns]
(statistics)
Two events are statistically independent if the probability of their occurring jointly equals the product of their respective probabilities. Also known as stochastic independence.

## statistical independence

See CORRELATION.
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References in periodicals archive ?
Therefore, we showed that it is always possible to define hidden variables satisfying the 2 PBR axioms: i) statistical independence at the sources or preparation
Sampling without replacement is well known to lead to violations of statistical independence between trials, which the PEAR analyses did not take into account, and which can lead to p values incorrect by several orders of magnitude.
In his next paragraph, Huebner states, "The author ignores the clear lack of expected randomness in many of the entries of Table 1." This also is mistaken: the article addresses randomness, expected or otherwise, in the sections on the error distribution, statistical independence, Benford's law, rounding, and the systematic properties expressed by the equation for longevity.
In case of diagonal covariance matrix (statistical independence of the class of features) there is formularization of the distance by the standardized Euclidean distance and classification rule is
The product of these two probabilities (.454 x .083 = .038), if we assume statistical independence and the rules of conditional probabilities, would be the probability of winning the TC [P(TC)].
(30) If the observed frequencies (survey results) differ significantly (< 0,5 per cent level of significance) from the expected frequencies, then the assumption of statistical independence is contradicted, and at least some relationship between the two variables is implied.
Utilizing groups themselves as the unit of analysis, rather than the individual scores, decreases the possibility of the statistical independence assumption being violated and systematic error being created (McMillan, 1999).
In addition, the faithfulness condition rules out those models in which statistical independence relations follow was a result of special coincidences among the parameter values.

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