• Type II error is defined as the probability of incorrectly rejecting the null hypothesis when in fact it does not apply to the entire population.

  • Type II error, in fact, is a false negative.
  • Type II error can be reduced by making the criteria for rejecting the null hypothesis more stringent, although this increases the chances of a false positive.
  • Sample size, true population size, and pre-set alpha level affect the amount of error risk.
  • Analysts need to weigh the likelihood and impact of Type II errors against Type I errors.