๐ Topic Summary
Parametric statistical tests rely on certain assumptions about the data being analyzed. These assumptions typically involve the distribution of the data (e.g., normality), the homogeneity of variances, and the independence of observations. If these assumptions are violated, the results of the parametric tests may not be valid. This worksheet will help you review and apply your understanding of these crucial assumptions.
Understanding and verifying these assumptions is essential before applying tests like t-tests or ANOVA. Failing to meet these assumptions can lead to inaccurate conclusions. Let's dive into practicing these concepts!
๐ง Part A: Vocabulary
Match the term with its correct definition:
| Term |
Definition |
| 1. Normality |
a. The extent to which data values are close to their mean. |
| 2. Homogeneity of Variance |
b. The assumption that data points are not influenced by each other. |
| 3. Independence |
c. The assumption that the variance within each population is equal. |
| 4. Outlier |
d. A data point that differs significantly from other observations. |
| 5. Parametric Test |
e. A statistical test that assumes data comes from a specific distribution. |
๐ Part B: Fill in the Blanks
Complete the following paragraph with the correct terms.
When conducting a t-test, one key assumption is __________. This means the data should approximate a bell-shaped curve. Another important assumption is __________ of __________, ensuring that the spread of data is similar across groups. Finally, __________ of observations is crucial, implying that each data point is not influenced by others. If these assumptions are severely violated, consider using __________ tests instead.
๐ค Part C: Critical Thinking
Explain why it is important to check the assumptions of parametric tests before interpreting the results. What are the potential consequences of ignoring these assumptions?