False Positive

Definition

A false positive is a test result that incorrectly indicates the presence of a condition or characteristic when it is actually not present. It occurs when a statistical test or diagnostic test wrongly identifies something as true, even though it is false.

5 Must Know Facts For Your Next Test

1. A false positive can occur in various statistical tests, such as hypothesis testing, where the null hypothesis is incorrectly rejected when it is actually true.
2. False positives can lead to unnecessary further testing, treatment, or interventions, which can be costly, time-consuming, and potentially harmful to the individual.
3. The probability of a false positive is represented by the significance level (α) in hypothesis testing, which is the maximum acceptable probability of a Type I error.
4. Reducing the significance level can decrease the likelihood of a false positive, but it may also increase the likelihood of a false negative (Type II error).
5. False positives can also occur in diagnostic tests, such as medical screenings, where a positive result indicates the presence of a condition that is actually not present.

Review Questions

• Explain how a false positive can occur in the context of null and alternative hypotheses.
  • In the context of null and alternative hypotheses, a false positive can occur when the null hypothesis is true (i.e., there is no significant difference or effect), but the statistical test incorrectly rejects the null hypothesis, leading to the conclusion that the alternative hypothesis is true. This type of error is known as a Type I error, and the probability of this occurring is represented by the significance level (α) chosen for the hypothesis test.
• Describe the relationship between false positives, Type I errors, and the significance level in hypothesis testing.
  • The probability of a false positive is directly related to the significance level (α) in hypothesis testing. The significance level represents the maximum acceptable probability of a Type I error, which is the error that occurs when the null hypothesis is true, but is rejected, leading to a false positive conclusion. By setting a lower significance level, the likelihood of a false positive can be reduced, but this may also increase the probability of a false negative (Type II error), where the null hypothesis is false, but is not rejected.
• Analyze the potential consequences of a false positive in the context of diagnostic testing and decision-making.
  • A false positive in diagnostic testing can have significant consequences for the individual, as it can lead to unnecessary further testing, treatment, or interventions, which can be costly, time-consuming, and potentially harmful. This can cause unnecessary anxiety and stress for the individual, as well as potentially leading to additional medical procedures or treatments that are not actually required. Furthermore, false positives can also impact decision-making processes, as they may lead to incorrect conclusions or actions being taken, which can have broader implications for individuals, organizations, or society as a whole.

Related terms

Type I Error: A Type I error is a statistical error that occurs when the null hypothesis is true, but is rejected, leading to a false positive conclusion.

Null Hypothesis: The null hypothesis is a statistical hypothesis that states that there is no significant difference between specified populations, or that a process has no effect.

Type II Error: A Type II error is a statistical error that occurs when the null hypothesis is false, but is not rejected, leading to a false negative conclusion.