Please choose ALL options that are correct from the list below. Statistical power = 1 - type II error rate. Type I and type II errors are inversely related. Type I error rates are determined by the significance level, alpha. Type II errors are inflated when we undertake a multiple comparisons test. Type I error rates report false negatives.
Question
Please choose ALL options that are correct from the list below. Statistical power = 1 - type II error rate. Type I and type II errors are inversely related. Type I error rates are determined by the significance level, alpha. Type II errors are inflated when we undertake a multiple comparisons test. Type I error rates report false negatives.
Solution
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Statistical power = 1 - type II error rate: This statement is correct. Statistical power is indeed defined as 1 minus the probability of making a Type II error (failing to reject the null hypothesis when it is false).
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Type I and type II errors are inversely related: This statement is not entirely correct. While it is true that reducing the probability of one type of error can increase the probability of the other type of error, they are not strictly inversely related. The relationship between the two error types also depends on other factors, such as the sample size and the effect size.
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Type I error rates are determined by the significance level, alpha: This statement is correct. The Type I error rate is indeed determined by the significance level, which is often denoted by the Greek letter alpha.
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Type II errors are inflated when we undertake a multiple comparisons test: This statement is not correct. It's actually the Type I error rate that can be inflated when multiple comparisons are made, not the Type II error rate.
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Type I error rates report false negatives: This statement is not correct. Type I errors are false positives, not false negatives. A Type I error occurs when we incorrectly reject the null hypothesis. A Type II error, on the other hand, is a false negative, occurring when we fail to reject the null hypothesis when it is false.
Similar Questions
How does the concept of statistical power relate to Type II Error? A. High statistical power reduces the risk of Type II Error B. High statistical power increases the risk of Type II Error C. Type II Error is not influenced by statistical power D. Statistical power is irrelevant in hypothesis testing
What is the relationship between the power of a statistical test and Type 2 error?a.Power and Type 2 error are inversely related.b.Power and Type 2 error are directly related.c.Power and Type 2 error are unrelated.d.Power is the same as Type 2 error.Clear my choice
The power of hypothesis test is (when a = Type I error and b = Type II error)
Power analysis helps enhance the reliability and validity of research findings by:Review LaterMinimizing the risk of Type I errorsMinimizing the risk of Type II errorsMaximizing the sample sizeMaximizing the effect size
In statistics, a Type I error is called a false positive. They occur when we mistakenly reject a hypothesis that is actually true. A Type II error is a false negative. They occur when we mistakenly accept a hypothesis that is actually false.Imagine you run an experiment to determine whether the kilometers per kilowatt(km/kWh) differs significantly between two models of electric vehicle: Tesla Model Y and Chevrolet Bolt. You hypothesize that there is no difference in their efficiency. Based on your findings, you determine your hypothesis is right and there is no difference between the km/kWh. Later, you learn that there is a significant difference in the km/kWh of these two models. What type of error did you commit?
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