1. What is Power Calculation in RCT?

Power calculation in randomized controlled trials (RCT) determines the probability that a study will detect an effect when there is a true effect present. Statistical power (1-β) is crucial for ensuring studies are adequately sized to answer research questions.

2. How Does the Calculator Work?

The calculator uses the power formula:

Where:

• \( \alpha \) — Significance level (Type I error rate)
• \( \delta \) — Effect size (clinically meaningful difference)
• \( \sigma \) — Standard deviation of the outcome
• \( n \) — Sample size per group
• \( \beta \) — Type II error rate

Explanation: Power increases with larger effect sizes, larger sample sizes, and higher significance levels, but decreases with greater variability in outcomes.

3. Importance of Power Calculation

Details: Adequate power (typically ≥80%) ensures studies can detect meaningful effects, prevents wasted resources on underpowered trials, and enhances research credibility and ethical conduct.

4. Using the Calculator

Tips: Enter significance level (usually 0.05), expected effect size, standard deviation from pilot data or literature, and planned sample size. All values must be positive and valid.

5. Frequently Asked Questions (FAQ)

Q1: What is an acceptable power level?
A: Typically 80% or higher is considered acceptable, though 90% is preferred for definitive trials.

Q2: How do I determine effect size?
A: Use clinically meaningful differences from previous studies, pilot data, or expert consensus on minimum important differences.

Q3: What if my power is too low?
A: Consider increasing sample size, using more precise measurements, or focusing on larger effect sizes if clinically relevant.

Q4: Does this account for dropouts?
A: No, you should inflate your sample size by expected dropout rate (e.g., add 10-20% more participants).

Q5: Can I use this for different study designs?
A: This calculator is for two-group parallel RCTs. Other designs (crossover, cluster) require different calculations.