Pub. date: 2007 | Online Pub. Date: September 15, 2007 | DOI: 10.4135/9781412952651 | Print ISBN: 9781412924702 | Online ISBN: 9781412952651 | Publisher:SAGE Publications, Inc.About this encyclopedia
Confidence Intervals/Hypothesis Testing/Effect Sizes
Nambury S. Raju & John C. Scott & Jack E. Edwards
Inferential statistics play a critical role in assessing whether training, tests, or other organizational interventions have an effect that can be reliably expected based on data collected from samples of organizational members. For example, the score from a selection test administered to a sample of 100 job applicants could be correlated with the test takers' subsequent performance ratings to determine how well the test predicts job performance. If we find that the correlation ( r ) calculated from the sample's test scores and ratings is .25, we are left with several questions: How does a sample-derived correlation of .25 compare with the correlation that could be obtained if we had test and ratings data on all cases in the population? How likely is it that we will get the same or approximately the same value for r with another sample of 100 cases? How good is an r of Using ...