Statisticians distinguish between two types of errors

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AVP, selection systems can never be 100% valid. Some degree of error in assessment will always enter into every system. Statisticians distinguish between two types of errors. Alpha or Type I error (also known as a “false positive”) means making a job offer to someone whom your selection system tells you will be a great productive employee, only to find out later that he/she is not what you expected. Call this “taking some bad with the good.” Beta or Type II error (also known as a “false negative”) means NOT making a job offer to someone whom your selection system shows you SHOULDN’T hire, only to find out later that he/she would have been a great employee. Call this “losing some good with the bad.”

Provide an example of a selection situation where you would rather make an alpha error and an example of a different situation where you would rather make a beta error. Elaborate on why you would rather err in these different ways. What internal and external factors would lead you to be more willing to lean toward one or the other type of error?

Reference no: EM132190515

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