16 May 2018

🔬Data Science: Type II Error (Definitions)

"Within a hypothesis test, a type II error is the error of incorrectly not rejecting a null hypothesis when it should be rejected." (Glenn J Myatt, "Making Sense of Data: A Practical Guide to Exploratory Data Analysis and Data Mining", 2006)

"A type of error used in hypothesis testing that occurs when the test decision incorrectly “accepts” the null hypothesis. Based on the test statistic, the final decision fails to reject the Null when it is actually false. Type II error also is called 'beta' (β), and the default is typically set at 20%." (Lynne Hambleton, "Treasure Chest of Six Sigma Growth Methods, Tools, and Best Practices", 2007)

"A term that refers to failing to reject a null hypothesis when it is false. It is also sometimes termed a false negative and used when an outcome is incorrectly identified as not having happened, such as when a customer has committed fraud but has not been accurately identified." (Evan Stubbs, "Delivering Business Analytics: Practical Guidelines for Best Practice", 2013)

"Nonrejection of the null hypothesis when it's false." (Geoff Cumming, "Understanding The New Statistics", 2013)

"When the system accepts impostors who should be rejected (false acceptance rate)." (Adam Gordon, "Official (ISC)2 Guide to the CISSP CBK" 4th Ed., 2015)

"Probability of not rejecting the null hypothesis when the null hypothesis is false." (K  N Krishnaswamy et al, "Management Research Methodology: Integration of Principles, Methods and Techniques", 2016)

"Probability of not rejecting the null hypothesis when it's false." (Geoff Cumming, "Understanding The New Statistics", 2013)

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