📚 Topic Summary
Parametric statistical tests rely on certain assumptions about the population data. These assumptions typically include that the data is normally distributed, has equal variances across groups (homogeneity of variance), and is measured on an interval or ratio scale. When these assumptions are violated, the results of parametric tests may be unreliable, leading to incorrect conclusions. It’s crucial to check these assumptions before applying parametric tests like t-tests or ANOVA.
This worksheet will help you practice identifying and understanding these assumptions through various exercises. Let’s get started!
🧠 Part A: Vocabulary
Match the following terms with their correct definitions:
| Term |
Definition |
| 1. Normality |
A. The spread of data points around the mean is similar across groups. |
| 2. Homogeneity of Variance |
B. Data follows a bell-shaped curve. |
| 3. Independence |
C. Data is measured on a continuous scale with equal intervals. |
| 4. Interval Scale |
D. Each data point is not influenced by other data points. |
| 5. Parametric Test |
E. A statistical test that assumes data comes from a specific distribution. |
✍️ Part B: Fill in the Blanks
Complete the following paragraph with the correct words:
Before conducting a $______$ test, it is essential to check the $______$ of the data. One common assumption is $______$, which means the data should follow a normal distribution. Another assumption is $______$, ensuring variances are equal across groups. Violating these assumptions can lead to $______$ conclusions.
🤔 Part C: Critical Thinking
Explain why it is important to check parametric assumptions before conducting statistical tests. What are the potential consequences of ignoring these assumptions?