The 5 Factors That Determine Statistical Power. #impactevaluation #education #statistics

Understanding statistical power is essential for designing strong, reliable, and efficient experiments. In this video, we break down the five core elements that influence statistical power — significance level (α), power (κ), true effect size (β), variance (σ²), and sample size (n) — and show how they all fit together through one elegant equation.

You’ll learn:
✅ What statistical power really means
✅ How α, κ, β, σ², and n interact
✅ The concept of Minimum Detectable Effect (MDE)
✅ Why increasing sample size improves — but not infinitely — your ability to detect effects
✅ How researchers balance precision, feasibility, and cost

Whether you’re a data analyst, researcher, or student, this step-by-step explanation will help you visualize and understand how power analysis works in real-world studies.

🎓 Watch till the end to see real examples of how changing sample size affects detectability — and learn how to plan your own studies with confidence.

🔔 Subscribe to MEARL ADVISOR for more videos on statistics, data analysis, and evidence-based research design.

#Statistics, #PowerAnalysis, #HypothesisTesting, #ResearchMethods, #DataScience, #SampleSize, #StatisticalPower, #MDE, #EffectSize, #MEARLAdvisor @mearladvisor_1