📚 Topic Summary
Power analysis is a crucial part of statistical hypothesis testing. It helps us determine the probability that a test will correctly reject a false null hypothesis. In simpler terms, it tells us how likely our study is to detect a real effect if one exists. Key factors in power analysis include the significance level (alpha), sample size, effect size, and the desired power (1 - beta). Understanding these elements allows researchers to design effective studies and interpret results with confidence.
🧠 Part A: Vocabulary
Match each term with its correct definition:
| Term |
Definition |
| 1. Power |
A. The probability of rejecting a true null hypothesis (Type I error). |
| 2. Significance Level ($\alpha$) |
B. The magnitude of the difference between groups or variables. |
| 3. Effect Size |
C. The probability of failing to reject a false null hypothesis (Type II error). |
| 4. Type I Error |
D. The probability of correctly rejecting a false null hypothesis. |
| 5. Type II Error ($\beta$) |
E. The threshold for rejecting the null hypothesis. |
(Match the numbers 1-5 with the letters A-E)
📝 Part B: Fill in the Blanks
Complete the following paragraph with the correct words:
______ analysis is essential before conducting a study to determine the necessary ______ size. A higher ______ indicates a greater chance of detecting a true effect. The significance level, often set at ______, represents the probability of a Type I error. Conversely, beta ($\beta$) is the probability of a ______ error.
(Word Bank: Sample, Power, Power, Type II, 0.05)
💡 Part C: Critical Thinking
Explain how increasing the sample size generally affects the power of a statistical test. Why is this important in research?
