Type II Error

Type II Error - An incorrect decision to accept something when it is unacceptable.

[Category=Quality ]

Source: American Society for Quality, 27 October 2010 08:35:51, http://www.asq.org/glossary/

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Type II Error - In hypotheis testing: failing to reject a false null hypothesis (e.g., failing to convict a guilty person). TYPE 2 errors are those where scientists assumed no relationship exists when in fact it does.

Consumers Risk / Consumer's Risk - Accepting and shipping bad parts.

[Category=Data Quality ]

Source: iSixSigma, 01 March 2011 08:49:04, https:web.archive.org/web/20111109014246/http:www.isixsigma.com/index.php?option=com_glossary

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Type I & Type II Errors - Type I error (also known as alpha error) - conclude a difference exists when no difference exists. (for example, you say two machines produce different mean outputs when they do not.).

Type II error (also known as beta error) - conclude no difference exists when it does. (for example, say two machines produce similar mean outputs when in fact they do).

Notes:

a) for fixed sample size experiments, reducing Type I errors result in higher Type II errors. (and vice versa) b) increase in sample size (n), generally reduces both types of errors c) very large sample sizes may result in detecting "statistically significant, but practically insignificant results".

To determine if something is statistically significant, we typically calculate a p-value. To determine statistical significance - a) if p-value is <= alpha, conclude statistical difference, b) if p-value is > alpha, fail to conclude difference. For most experiments: let alpha = 0.01 or 0.05; may tighten alpha if effect of Type I Error is very severe. In terms of statistical significance, (1-p) represents your confidence that a statistically significant difference exists.

[Category=Quality ]

Source: The Quality Portal, 28 April 2011 08:51:50, http://thequalityportal.com/glossary/s.htm