Probability Of Committing A Type 1 Error
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by the level of significance and the power for the test. Therefore, you should determine which error has more severe consequences for your situation before you define their risks. No hypothesis probability of type 2 error test is 100% certain. Because the test is based on probabilities, there is always type 1 error example a chance of drawing an incorrect conclusion. Type I error When the null hypothesis is true and you reject it, you type 3 error make a type I error. The probability of making a type I error is α, which is the level of significance you set for your hypothesis test. An α of 0.05 indicates that you are
What Is The Probability Of A Type I Error For This Procedure
willing to accept a 5% chance that you are wrong when you reject the null hypothesis. To lower this risk, you must use a lower value for α. However, using a lower value for alpha means that you will be less likely to detect a true difference if one really exists. Type II error When the null hypothesis is false and you fail to reject it, you make a type II what is the probability that a type i error will be made error. The probability of making a type II error is β, which depends on the power of the test. You can decrease your risk of committing a type II error by ensuring your test has enough power. You can do this by ensuring your sample size is large enough to detect a practical difference when one truly exists. The probability of rejecting the null hypothesis when it is false is equal to 1–β. This value is the power of the test. 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) Reject Type I Error - rejecting the null when it is true (probability = α) Correct Decision (probability = 1 - β) Example of type I and type II error To understand the interrelationship between type I and type II error, and to determine which error has more severe consequences for your situation, consider the following example. A medical researcher wants to compare the effectiveness of two medications. The null and alternative hypotheses are: Null hypothesis (H0): μ1= μ2 The two medications are equally effective. Alternative hypothesis (H1): μ1≠ μ2 The two medications are not e
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