When assessing the statistical significance of a study, what is the most important consideration?

Correct answer: D. Type I and Type II errors

• A. INCORRECT — Confidence intervals provide useful information about precision, but they are a related concept, not the primary determinant of significance.
• B. INCORRECT — The number of dependent variables affects study design complexity but is not the primary consideration when evaluating statistical significance.
• C. INCORRECT — Sample size is important (it affects power/Type II error), but it is a design parameter rather than the primary conceptual consideration for statistical significance.
• D. CORRECT — Type I error (alpha; rejecting a true null hypothesis — false positive) and Type II error (beta; failing to reject a false null hypothesis — false negative) are the foundational concepts in determining statistical significance. The p-value directly controls Type I error risk. Power analysis addresses Type II error.