type 1 and 2 errors

  • related: Biostats
  • tags: #literature #pulmonary

  • type 1 error: false positive
  • type 2 error: false negative
  • significance level = alpha (α): probability of making type 1 error
  • beta (β): probability of making type 2 error

Hypothesis Testing

  • Type II error 1

Statistical analysis of various studies hinges on significance testing. Two types of sampling errors exist, known as type I and type II, and also termed α and β errors. A type I error is a false-positive conclusion, resulting from rejecting a null hypothesis that is correct. A type II error is a false-negative conclusion, meaning a failure to reject a null hypothesis that is false.

Type I errors are typically emphasized as the most important to avoid in statistical analyses, but the type I error (a false-positive conclusion) is actually only possible if the null hypothesis is true. If the null hypothesis is false, a type I error is impossible, but a type II error, the false-negative conclusion, can occur. One way to reduce type II statistical error is to increase the sample sizes of the cohorts studied.

Alpha (α) represents the probability of type I error, meaning concluding there is a difference in the study subjects receiving each of the drugs you are studying, when in fact no difference actually exists. Beta (β) is the probability of type II error—that is, concluding there is no difference in study groups, when in fact there truly is a difference. The greater the statistical power of your study, the less the probability there is of making a type II error (Figure 1). Larger sample sizes in the studies after yours reduced sampling error and increased, not decreased, statistical power. The data you reviewed in your clinical trial in this case led you to conclude that the null hypothesis of no difference between the two groups was valid when, in fact, you should have rejected the null hypothesis—in other words, you had a false-negative result. This represents type II, not type I, statistical error.2

Footnotes

  1. Mayo Foundation for Medical Education and Research. ↩

  2. SEEK Questionnaires ↩