The Minimally Important Difference Significant Criterion for Sample Size

@inproceedings{Harris1992TheMI,
  title={The Minimally Important Difference Significant Criterion for Sample Size},
  author={Richard H. Jr. Harris and Dana Quade},
  year={1992}
}
For a wide range of tests of single-df hypotheses, the sample size needed to achieve 50% power is readily approximated by setting N such that a significance test conducted on data that fit one’s assumptions perfectly just barely achieves statistical significance at one’s chosen alpha level. If the effect size assumed in establishing one’s N is the minimally important effect size (i.e., that effect size such that population differences or correlations smaller than that are not of any… CONTINUE READING