Quantile Estimation
Quantile estimation is a statistical technique used to estimate specific points (quantiles) in a data distribution, such as the median (50th percentile) or 95th percentile. It involves calculating or approximating values that divide a dataset into equal-sized subsets, providing insights into data spread, outliers, and performance metrics like latency. This is crucial in fields like data analysis, finance, and system monitoring for understanding distribution tails and making data-driven decisions.
Developers should learn quantile estimation when working with large datasets, performance monitoring, or risk analysis, as it helps identify outliers, set service-level objectives (SLOs), and analyze latency distributions in systems. It is essential for tasks like A/B testing, financial modeling, and optimizing application performance by focusing on worst-case scenarios rather than averages.