Biostatistics & Research · single question
In statistical analysis of a clinical study, what is the MOST important factor in achieving statistical significance?
Show answer and explanation
Correct answer: B. Controlling for Type I and Type II errors
B) Type I and Type II errors — CORRECT.
Statistical significance is fundamentally about controlling error rates: Type I error (α, false positive — rejecting a true null hypothesis, typically set at 0.05) and Type II error (β, false negative — failing to reject a false null hypothesis). Power (1-β) and the p-value (α threshold) define whether results are considered statistically significant. These are the foundational concepts of hypothesis testing.
A) Number of dependent variables: Affects analysis choice (ANOVA, MANOVA) but is not the primary determinant of statistical significance.
C) Sample size: Affects POWER (ability to detect effect) which is related to Type II error — but the broader concept is controlling both error types.
D) Software: Irrelevant to the conceptual basis of statistical significance.
Statistical significance is fundamentally about controlling error rates: Type I error (α, false positive — rejecting a true null hypothesis, typically set at 0.05) and Type II error (β, false negative — failing to reject a false null hypothesis). Power (1-β) and the p-value (α threshold) define whether results are considered statistically significant. These are the foundational concepts of hypothesis testing.
A) Number of dependent variables: Affects analysis choice (ANOVA, MANOVA) but is not the primary determinant of statistical significance.
C) Sample size: Affects POWER (ability to detect effect) which is related to Type II error — but the broader concept is controlling both error types.
D) Software: Irrelevant to the conceptual basis of statistical significance.
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