Statistical Power
Statistical power is the probability that a statistical test will correctly reject a false null hypothesis, meaning it detects an effect when one truly exists. It is a key concept in hypothesis testing and experimental design, often expressed as 1 - β, where β is the Type II error rate. High statistical power reduces the risk of false negatives and ensures that studies are adequately sensitive to detect meaningful effects.
Developers should learn statistical power when designing A/B tests, analyzing user behavior data, or conducting experiments in machine learning to ensure reliable results. It is crucial for determining appropriate sample sizes, avoiding wasted resources on underpowered studies, and making data-driven decisions with confidence in fields like web analytics, product development, and data science.