What Should Oncology Nurses Know About Type I and Type II Errors in a Clinical Study? (2024)

FAQs

What are Type I and Type II errors Why should we care? ›

A Type I error refers to the incorrect rejection of a true null hypothesis (a false positive). A Type II error is the acceptance of the null hypothesis when a true effect is present (a false negative). The more statistical comparisons performed in a given analysis, the more likely a Type I or Type II error is to occur.

Type I: A cancer patient believes the cure rate for the drug is less than 75% when it actually is at least 75%. Type II: A cancer patient believes the experimental drug has at least a 75% cure rate when it has a cure rate that is less than 75%.

A type II error occurs when we declare no differences or associations between study groups when, in fact, there was.

A type I error (false-positive) occurs if an investigator rejects a null hypothesis that is actually true in the population; a type II error (false-negative) occurs if the investigator fails to reject a null hypothesis that is actually false in the population.

Type II errors are like “false negatives,” an incorrect rejection that a variation in a test has made no statistically significant difference. Statistically speaking, this means you're mistakenly believing the false null hypothesis and think a relationship doesn't exist when it actually does.

A type II error produces a false negative, also known as an error of omission. For example, a test for a disease may report a negative result when the patient is infected. This is a type II error because we accept the conclusion of the test as negative, even though it is incorrect.

A Type II error means not rejecting the null hypothesis when it's actually false. This is not quite the same as “accepting” the null hypothesis, because hypothesis testing can only tell you whether to reject the null hypothesis.

Stage 2 usually means that the tumour is larger than in stage 1 but the cancer hasn't started to spread into the surrounding tissues. Sometimes stage 2 means that cancer cells have spread into lymph nodes close to the tumour. This depends on the particular type of cancer. Stage 3 usually means the cancer is larger.

The rate of a type II error (i.e., the probability of a type II error) is measured by beta (β) while the statistical power is measured by 1- β.

The only way to minimize type 1 errors, assuming you're A/B testing properly, is to raise your level of statistical significance.

What is Type 1 error control in clinical trials? ›

It is a commonly accepted principle within two-arm RCTs that it is important to control the chance of incorrectly recommending ineffective experimental treatments. In hypothesis testing language this is the type I error rate, which is the chance of incorrectly rejecting a true null hypothesis.

A Type II error is committed when we fail to reject a null hypothesis that is, in reality, not true.

In hypothesis testing, a Type II error occurs when the null hypothesis is not rejected even though it is false. The probability of committing Type II errors can be reduced by increasing the sample size and the statistical significance.

Consequently, a type 1 error will bring in a false positive. This means that you will wrongfully assume that your hypothesis testing has worked even though it hasn't. In real-life situations, this could potentially mean losing possible sales due to a faulty assumption caused by the test.

Pr(Type I error) = Pr(Reject H0| H0 is true)=α. However, in general, the probability of making Type II error, Pr(Type II error) = Pr(Not Reject H0| H0 is false), is different across different test statistics. The power of test is defined as Power = 1-Pr(Type II error) = 1-Pr(Not Reject H0| H0 is false).

Type 1 errors occur when you incorrectly assert your hypothesis is accurate, overturning previously established data in its wake. If type 1 errors go unchecked, they can ripple out to cause problems for researchers in perpetuity.

Question 2 (0.5 points) Type II errors concern psychologists because: future research might be based on results mistakenly declared significant. they could mean that good theories or useful practical procedures are not used. rejecting the null hypothesis should only occur when the research hypothesis is true.

For statisticians, a Type I error is usually worse. In practical terms, however, either type of error could be worse depending on your research context. A Type I error means mistakenly going against the main statistical assumption of a null hypothesis.

Type I error. False positive: rejecting the null hypothesis when the null hypothesis is true. Type II error. False negative: fail to reject/ accept the null hypothesis when the null hypothesis is false.