⚡ What is “Power” in Research?

The power of a study is the probability that your statistical test will correctly reject the null hypothesis when the alternative hypothesis is actually true.

• In other words, it's your study’s ability to detect a true effect.

• It is mathematically defined as:
  Power = 1 – β, where β is the probability of making a Type II error (false negative).

🧪 Why Does Study Power Matter in Research?

1. ✅ Avoids False Conclusions
   An underpowered study may fail to detect real differences or associations, leading to misleading results.

2. 📈 Supports Evidence-Based Practice
   Especially in clinical research, well-powered studies contribute to treatment guidelines and medical decision-making.

3. 📉 Prevents Resource Waste
   Time, money, and effort are wasted on studies that are unlikely to produce meaningful results.

4. 📊 Enhances Publication Quality
   Journals often require a power analysis to demonstrate that a study is methodologically sound.

🧠 Key Factors Affecting Power:

• Sample Size: More participants = more power

• Effect Size: Larger true differences = easier to detect

• Significance Level (α): Lower alpha = lower power

• Variability in Data: More variation = harder to detect effects

• Study Design: Matched pairs, crossovers, or repeated measures can increase power

💡 Typical Threshold:

Most research aims for at least 80% power, meaning there's an 80% chance your study will detect a true effect (if it exists).

🔢 Example:

Let’s say you are studying whether a new drug reduces blood pressure compared to a standard drug.

• Your estimated effect size (difference between means) = 5 mmHg

• You set α = 0.05 (5% chance of Type I error)

• You want 80% power to detect this difference

• You calculate (using G*Power or a sample size formula) that you need 150 patients in each group.

Now imagine you only enroll 50 per group.
Your power drops to ~45%, meaning even if the drug truly works, there’s more than a 50% chance your study won’t detect it. That’s a waste of time, resources, and opportunity—and potentially harmful if decisions are based on a false negative.

🛠️ What Affects Power?

• Sample Size ↑ → Power ↑

• Effect Size ↑ → Power ↑

• Variability (Standard Deviation) ↑ → Power ↓

• Alpha Level (Significance Threshold) ↓ → Power ↓

• Study Design Efficiency ↑ → Power ↑