Power of a test is:
A. ability to detect the real effect in a population based on a sample taken from the population B. Probability of correctly rejecting the null hypothesis when it is true C. increased when sample size increases D. Probability of correctly rejecting the null hypothesis when it is false E. decreased when sample size increases
Choose the correct answer from the options given below:

Correct option is C

Power of a test refers to the probability of correctly rejecting the null hypothesis (H₀) when it is actually false. A higher power means a higher likelihood of detecting a real effect in the population.
Key aspects of Power of a test include:
· Ability to detect the real effect in a population based on a sample taken from the population (A) – The test's power indicates how well it identifies a true effect rather than failing to detect it.
· Increased when sample size increases (C) – A larger sample size reduces variability, making it easier to detect true effects, thus increasing power.
· Probability of correctly rejecting the null hypothesis when it is false (D) – This is the formal definition of statistical power.
Information Booster:
Key Concepts in Hypothesis Testing:
1. Power (1 - β) – The ability of a test to detect a real effect when one exists.
2. Type I Error (α) – Rejecting a true null hypothesis (False Positive).
3. Type II Error (β) – Failing to reject a false null hypothesis (False Negative).
How to Increase Power of a Test?
Increase sample size – Reduces variability and enhances accuracy. Increase effect size – Stronger effects are easier to detect. Use a higher significance level (α) – But this also increases Type I error risk. Reduce variability in data – Leads to more precise estimates.
Additional Knowledge:
· Probability of correctly rejecting the null hypothesis when it is true (B) – This describes a Type I error, not statistical power.
· Decreased when sample size increases (E) – Power increases with larger sample sizes, making this statement incorrect.