11. What is the effect on the power of the test for the following scenarios: a. Bis increased b. Bis decreased c. a is increased d. a is decreased e. n is increased f. n is decreased

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### Understanding the Impact on Test Power

The power of a statistical test is influenced by various factors. Let's examine how the power of the test changes under the following scenarios:

1. **What is the effect on the power of the test for the following scenarios:**

    a. β (beta) is increased  
    b. β (beta) is decreased  
    c. α (alpha) is increased  
    d. α (alpha) is decreased  
    e. n (sample size) is increased  
    f. n (sample size) is decreased  

---

#### Explanation:

- **a. β (beta) is increased:**  
  Beta (β) represents the probability of making a Type II error, which is failing to reject a false null hypothesis. If β is increased, it means that the likelihood of making a Type II error is higher, and thus the power of the test (which is 1 - β) decreases.

- **b. β (beta) is decreased:**  
  Conversely, if β is decreased, the probability of making a Type II error is lower, which means the power of the test increases.

- **c. α (alpha) is increased:**  
  Alpha (α) is the probability of making a Type I error, which is rejecting a true null hypothesis. By increasing α, you also increase the test's power, since the critical region for rejecting the null hypothesis becomes larger.

- **d. α (alpha) is decreased:**  
  Decreasing α results in a smaller critical region, which in turn decreases the power of the test, as there's a lower probability of rejecting the null hypothesis when it is false.

- **e. n (sample size) is increased:**  
  Increasing the sample size (n) generally increases the power of the test. A larger sample size provides more information about the population, reducing the standard error and making it easier to detect a true effect.

- **f. n (sample size) is decreased:**  
  Decreasing the sample size reduces the power of the test. With a smaller sample size, there is higher variability in the data, making it more difficult to detect a true effect.

Understanding how these factors influence the power of a test is crucial for designing experiments and making accurate inferences from data.
Transcribed Image Text:### Understanding the Impact on Test Power The power of a statistical test is influenced by various factors. Let's examine how the power of the test changes under the following scenarios: 1. **What is the effect on the power of the test for the following scenarios:** a. β (beta) is increased b. β (beta) is decreased c. α (alpha) is increased d. α (alpha) is decreased e. n (sample size) is increased f. n (sample size) is decreased --- #### Explanation: - **a. β (beta) is increased:** Beta (β) represents the probability of making a Type II error, which is failing to reject a false null hypothesis. If β is increased, it means that the likelihood of making a Type II error is higher, and thus the power of the test (which is 1 - β) decreases. - **b. β (beta) is decreased:** Conversely, if β is decreased, the probability of making a Type II error is lower, which means the power of the test increases. - **c. α (alpha) is increased:** Alpha (α) is the probability of making a Type I error, which is rejecting a true null hypothesis. By increasing α, you also increase the test's power, since the critical region for rejecting the null hypothesis becomes larger. - **d. α (alpha) is decreased:** Decreasing α results in a smaller critical region, which in turn decreases the power of the test, as there's a lower probability of rejecting the null hypothesis when it is false. - **e. n (sample size) is increased:** Increasing the sample size (n) generally increases the power of the test. A larger sample size provides more information about the population, reducing the standard error and making it easier to detect a true effect. - **f. n (sample size) is decreased:** Decreasing the sample size reduces the power of the test. With a smaller sample size, there is higher variability in the data, making it more difficult to detect a true effect. Understanding how these factors influence the power of a test is crucial for designing experiments and making accurate inferences from data.
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