type II error


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error

 [er´or]
a defect or mistake in structure or function.
inborn error of metabolism a genetically determined biochemical disorder in which a specific enzyme defect produces a metabolic block that may have pathologic consequences at birth, as in phenylketonuria, or in later life.
measurement error the difference between what exists in reality and what is measured by a measurement method.
Type I error the rejection of a null hypothesis that is true.
Type II error acceptance of a null hypothesis that is false.

er·ror of the sec·ond kind

in a Neyman-Pearson test of a statistical hypothesis, the probability of accepting the null hypothesis when it is false; the complement of the power of the test.
Synonym(s): beta error, type II error

type II error

in a test of a statistical hypothesis, the probability of accepting the null hypothesis when it is false and should be rejected. Also called beta error, error of the second kind.

type II error

β error, false-negative error Statistics Acceptance of the null hypothesis when it is false or incorrect, or the error of falsely stating that two proportions are not significantly different when they actually are. See Null hypothesis Cf Type 1 error.
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References in periodicals archive ?
To estimate the type II error rate, n = 10,000 independent and identically Poi([lambda].
A Data-Driven Diagnostic System Utilizing Manufacturing Data Mining and Analytics
Finally, our analysis suggests a real need to more carefully consider tradeoffs between Type I and Type II error in this litigation setting.
Event studies in securities litigation: low power, confounding effects, and bias
The type II error rate is the probability of the algorithm not labeling the receiving leg as part of a fed funds loan conditional on the receiving leg being part of a fed funds loan.
Challenges in identifying interbank loans
Whether these trends are true changes in disease incidence or artifacts of changing reporting or diagnostic practices, anything that causes disease counts in the baseline period to be systematically higher than current disease counts increases type II errors, and anything that causes baseline disease counts to be systematically lower than current disease counts increases type I errors.
Refining historical limits method to improve disease cluster detection, New York City, New York, USA
If policymakers are more concerned with Type II errors (false negatives), then an appropriate policy prescription appeals to higher levels of significance as the relevant thresholds.
Dominance thresholds: a cautionary note
For crisis type II error (weakening when unjustified), the tendency of the leading statistics to reflect an emerging crisis rather slowly makes this type of error fairly likely for our rule-bound suggested approach.
Designing countercyclical capital buffers
Analytical results demonstrate that the prediction models by hybrid learning techniques perform better than any single classification technique in terms of prediction accuracy and the Type II error.
Project dispute prediction by hybrid machine learning techniques
This potentially resulted in 40 analyses (18% of all analyses in this study) that were a result of Type II error.
Research quality: critique of quantitative articles in the Journal of Counseling & Development
05) and no more than a 1 in 5 chance of making a type II error ([beta] = 20%; power = 80%, 1-[beta]).
Errors in the interpretation of 'no statistically significant difference'
As for the ecological approach, when policymakers decide not to act and are wrong, they locate in the lower left quadrant (III), representing a Type II error.
Decisionmaking, risk, and uncertainty: an analysis of climate change policy
Panel B of Table 4 contains the within-sample Type I error rates for various levels of Type II error rates corresponding to the predictions from the logistic regressions.
The role of RBC, hurricane exposure, bond portfolio duration, and nacroeconomic and industry-wide factors in property-liability insolvency prediction
Type II error is when we have failed to reject the null hypothesis when, in fact, it is false.
Hypothesis testing: a look at two common approaches: depending upon the situation, either classical or Bayesian testing may be the better choice

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