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# examples type i error Chimacum, Washington

A medical researcher wants to compare the effectiveness of two medications. Minitab.comLicense PortalStoreBlogContact UsCopyright © 2016 Minitab Inc. Null Hypothesis Decision True False Fail to reject Correct Decision (probability = 1 - α) Type II Error - fail to reject the null when it is false (probability = β) If the medications have the same effectiveness, the researcher may not consider this error too severe because the patients still benefit from the same level of effectiveness regardless of which medicine

Because the test is based on probabilities, there is always a chance of drawing an incorrect conclusion. An α of 0.05 indicates that you are willing to accept a 5% chance that you are wrong when you reject the null hypothesis. You can do this by ensuring your sample size is large enough to detect a practical difference when one truly exists. No hypothesis test is 100% certain.

This error is potentially life-threatening if the less-effective medication is sold to the public instead of the more effective one. Type II error When the null hypothesis is false and you fail to reject it, you make a type II error. menuMinitab® 17 SupportWhat are type I and type II errors?Learn more about Minitab 17  When you do a hypothesis test, two types of errors are possible: type I and type II. The null and alternative hypotheses are: Null hypothesis (H0): μ1= μ2 The two medications are equally effective.

The probability of rejecting the null hypothesis when it is false is equal to 1–β. The probability of making a type II error is β, which depends on the power of the test. As you conduct your hypothesis tests, consider the risks of making type I and type II errors. You can decrease your risk of committing a type II error by ensuring your test has enough power.

A type I error occurs if the researcher rejects the null hypothesis and concludes that the two medications are different when, in fact, they are not. That is, the researcher concludes that the medications are the same when, in fact, they are different. However, if a type II error occurs, the researcher fails to reject the null hypothesis when it should be rejected. The probability of making a type I error is α, which is the level of significance you set for your hypothesis test.

Alternative hypothesis (H1): μ1≠ μ2 The two medications are not equally effective. The risks of these two errors are inversely related and determined by the level of significance and the power for the test. Type I error When the null hypothesis is true and you reject it, you make a type I error. However, using a lower value for alpha means that you will be less likely to detect a true difference if one really exists.

If the consequences of making one type of error are more severe or costly than making the other type of error, then choose a level of significance and a power for Therefore, you should determine which error has more severe consequences for your situation before you define their risks. This value is the power of the test. All rights Reserved.EnglishfrançaisDeutschportuguêsespañol日本語한국어中文（简体）By using this site you agree to the use of cookies for analytics and personalized content.Read our policyOK

To lower this risk, you must use a lower value for α.