📚 Topic Summary
In hypothesis testing, we use sample data to evaluate a claim (hypothesis) about a population. The type of test we use depends on the directionality of our hypothesis. A one-tailed test is used when we predict the effect will be in a specific direction (either greater than or less than a certain value). For example, we might hypothesize that a new drug increases test scores. A two-tailed test is used when we predict an effect, but don't specify the direction. For instance, we might hypothesize that a drug changes test scores (either increases or decreases).
The key difference lies in how we calculate the p-value and define the critical region. One-tailed tests have all the alpha level (significance level) in one tail of the distribution, making it easier to reject the null hypothesis if the effect is in the predicted direction. Two-tailed tests split the alpha level between both tails, requiring a larger effect to reject the null hypothesis.
🔤 Part A: Vocabulary
Match the terms with their definitions:
| Term |
Definition |
| 1. Null Hypothesis |
A. The probability of observing a test statistic as extreme as, or more extreme than, the one computed if the null hypothesis is true. |
| 2. Alternative Hypothesis |
B. A test where the critical region is only in one tail of the distribution. |
| 3. P-value |
C. The statement being tested, typically a statement of no effect or no difference. |
| 4. One-tailed test |
D. The statement that contradicts the null hypothesis. |
| 5. Two-tailed test |
E. A test where the critical region is in both tails of the distribution. |
✍️ Part B: Fill in the Blanks
Complete the following paragraph using the words: direction, significance level, reject, two-tailed, one-tailed.
When conducting a hypothesis test, choosing between a ______ test and a ______ test is crucial. A one-tailed test is appropriate when the ______ of the effect is predicted. The ______ determines the threshold for deciding whether to ______ the null hypothesis.
🤔 Part C: Critical Thinking
Explain, in your own words, why it is important to decide whether to use a one-tailed or two-tailed test before analyzing your data. What are the potential consequences of making this decision after seeing the results?