Hypothesis testing is a crucial part of statistical analysis, allowing us to make inferences about a population based on sample data. However, it's easy to fall into common traps. These include misinterpreting p-values, confusing statistical significance with practical significance, and failing to account for multiple comparisons. Understanding these pitfalls is essential for drawing valid conclusions from your research.
This worksheet will help you identify and avoid these common errors, ensuring your hypothesis testing is both accurate and meaningful. It will cover common vocabulary, require you to fill in the blanks to show understanding, and have you critically think about how to make accurate decisions.
Match the term with its correct definition:
| Term | Definition |
|---|---|
| 1. p-value | a. The probability of observing a test statistic as extreme as, or more extreme than, the statistic obtained from a sample if the null hypothesis is true. |
| 2. Type I Error | b. Rejecting the null hypothesis when it is actually true (false positive). |
| 3. Type II Error | c. Failing to reject the null hypothesis when it is actually false (false negative). |
| 4. Statistical Significance | d. A result is unlikely to occur by chance, assuming the null hypothesis is true. |
| 5. Null Hypothesis | e. A statement of no effect or no difference, that you are trying to disprove. |
Complete the following paragraph with the correct terms.
A ________ is the probability of observing a test statistic as extreme as, or more extreme than, the statistic obtained from a sample if the null hypothesis is true. A ________ occurs when we reject the null hypothesis when it's actually true, while a ________ occurs when we fail to reject the null hypothesis when it is actually false. ________ means that a result is unlikely to occur by chance, assuming the null hypothesis is true, but it doesn't necessarily mean the result is ________. The ________ is a statement of no effect or no difference, which we try to disprove.
Explain the difference between statistical significance and practical significance. Provide an example of a situation where a result might be statistically significant but not practically significant.
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