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Table of Contents

- Why is it generally inappropriate to report effect size with nonsignificant results?
- Which of the following is an advantage for selecting related samples compared to selecting independent samples in behavioral research?
- When a researcher decides to retain the null hypothesis because the rejection region was located in the wrong tail this is called?
- What is the typical level of significance for a hypothesis test in behavioral research?
- How do you get rid of type 1 error?
- What is the probability of a Type I error?
- How does an increase in the sample size affect the probability of making a Type I error?
- What is an accurate definition of a Type I error and what is the consequence of a type I error?

Why is it generally inappropriate to report effect size with insignificant results? Because insignificant results will always have an effect size equal to 0. Because insignificant results indicate that an effect size is also insignificant.

Which of the following is an advantage for selecting related samples compared to selecting independent samples in behavioral research? All of the above (Selecting related samples can be more practical.; Selecting related samples minimizes standard error.; Selecting related samples increases power.)

The one-sample z test is a hypothesis test used to test hypotheses. concerning a single population with a known variance. When a researcher decides to retain the null hypothesis because the rejection region was located in the wrong tail, this is called a. Type III error.

In behavioral science, the criterion or level of significance is typically set at 5%. When the probability of obtaining a sample mean is less than 5% if the null hypothesis were true, then we reject the value stated in the null hypothesis. A decision made in hypothesis testing centers on the null hypothesis.

One of the most common approaches to minimizing the probability of getting a false positive error is to minimize the significance level of a hypothesis test. Since the significance level is chosen by a researcher, the level can be changed. For example, the significance level can be minimized to 1% (0.01).

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 willing to accept a 5% chance that you are wrong when you reject the null hypothesis. The probability of rejecting the null hypothesis when it is false is equal to 1–β.

Caution: The larger the sample size, the more likely a hypothesis test will detect a small difference. So the probability of rejecting the null hypothesis when it is true is the probability that t > tα, which we saw above is α. In other words, the probability of Type I error is α.

rejecting a true null hypothesis. Which of the following is an accurate definition of a Type II error? failing to reject a false null hypothesis. What is the consequence of a Type I error? Concluding that a treatment has an effect when it really has no effect.