The article introduces Meituan’s trusted experiment analysis engine, detailing its rich methodological library, system architecture, integration options, and a step‑by‑step offline analysis case that together empower teams to conduct reliable, efficient A/B tests without deep statistical expertise.
This comprehensive guide introduces twenty essential advanced statistical methods—from Bayesian inference and maximum likelihood estimation to copulas and generalized additive models—explaining their concepts, real‑world use cases, and providing concise Python code examples so data scientists can confidently apply them to complex analytical problems.
This article explains how to design a complete metric‑abnormality pipeline—from real‑time threshold alerts and statistical tests such as 3σ, GESD, IQR, and MBP to trend analysis with Mann‑Kendall and Prophet, and finally to deterministic and probabilistic attribution using contribution decomposition and SHAP, all illustrated with practical business cases.
This article explores why outliers appear in large‑scale internet A/B tests, explains their impact on experiment precision, compares traditional trim and winsorize techniques, reviews a range of statistical and machine‑learning detection methods, and offers practical recommendations for handling them in product experiments.
This article introduces the Geodetector statistical method for measuring spatial stratified heterogeneity and demonstrates how to install and use the py_geodetector Python package, providing a complete code example for factor, interaction, ecological, and risk analysis.
This article examines the challenges of outliers in large‑scale internet A/B testing, explains their statistical definition, outlines common causes, evaluates the benefits and limits of removal, and compares traditional trim and winsorize techniques along with practical detection and risk‑control strategies.
The article explains why outliers destabilize internet A/B tests, outlines their causes, compares trimming and winsorizing, presents lightweight detection (e.g., kurtosis) and risk‑control strategies, and offers practical recommendations for bias‑aware removal and variance‑reduction techniques to improve experimental precision.
This article introduces eight core data analysis methods—including association, comparative, clustering, cross, Pareto, quadrant, funnel, and full‑path analysis—explaining their principles, typical use cases, key metrics, and visual examples to help professionals make data‑driven decisions.
The article presents a next‑generation experiment analysis engine that standardizes the core AB testing framework, integrates advanced statistical solutions to tackle small‑sample and overflow challenges, and offers precise variance and P‑value calculations, thereby improving reliability and efficiency for multi‑side fulfillment platform experiments.
The article examines the key challenges of recommendation AB testing at Xiaohongshu—including change stability, single‑experiment precision, and multi‑strategy packaging—and presents a series of engineering and statistical solutions such as SDK‑based AB architecture, virtual PreAA experiments, CUPED/DID adjustments, and reverse experiments to improve reliability and metric impact.
Structural Equation Modeling (SEM) combines multiple regression analyses to simultaneously assess direct and indirect relationships among observed and latent variables, offering advantages such as handling multiple causal paths, incorporating latent constructs, flexible error modeling, and testing mediation and moderation effects, illustrated with an education‑investment case study.
This article explains why traditional hypothesis‑testing methods can mis‑estimate variance when the splitting unit and analysis unit differ in AB experiments, introduces the Delta Method as an unbiased variance estimator, compares it with Bootstrap and other corrections through simulations and real‑world case studies, and highlights its computational efficiency.
This article introduces ten common statistical techniques used in data analysis—including descriptive statistics, correlation, t‑test, ANOVA, linear regression, PCA, outlier detection, frequency distribution, time‑series analysis, and non‑parametric tests—providing concise explanations and Python code snippets for each method.
This article presents NetEase Yanxuan's end‑to‑end approach for automatically detecting and diagnosing metric anomalies in e‑commerce, covering background motivation, three types of anomalies, statistical detection frameworks (GESD, volatility, Mann‑Kendall), post‑processing, contribution‑decomposition methods, dimension‑explosion challenges, optimization techniques, and a brief Q&A.
This article explains how to guarantee homogeneity in AB experiments by defining pre‑experiment bias, presenting statistical testing methods, outlining a three‑step workflow for both pre‑ and post‑experiment phases, and sharing real‑world case studies and correction techniques to improve decision‑making reliability.
This article presents NetEase Yanxuan's end‑to‑end approach for automatically detecting and diagnosing metric anomalies in e‑commerce, covering background motivations, statistical detection methods (absolute, volatility, trend), contribution‑decomposition diagnosis, optimization techniques for dimensional explosion, and a Q&A on practical implementation.
Box‑Cox transformation, introduced by Box and Cox in 1964, corrects skewed data to approximate normality by optimizing a λ parameter via maximum likelihood, enabling more accurate statistical modeling and machine‑learning predictions, as demonstrated with a crime‑rate dataset and Shapiro‑Wilk tests.
Learn how the Difference-in-Differences (DiD) method estimates policy impacts by comparing treatment and control groups over time, explore its mathematical model, see a concrete traffic‑restriction example, and follow a step‑by‑step Python implementation with data analysis and visualization.
This article explains the concept of A/B testing, details the architecture and implementation of an AB testing platform—including experiment, metric, whitelist, and traffic services—provides practical guidelines for experiment design, data reporting, statistical evaluation, and outlines future enhancements for product optimization.
Meituan's database platform team built an AI‑driven anomaly detection service that automatically extracts feature patterns, selects appropriate statistical algorithms, trains models, and performs both offline and online monitoring to quickly locate and mitigate database issues across diverse production scenarios.
Structural Equation Modeling (SEM) combines factor and path analysis to model relationships among observed and latent variables, handling measurement error and allowing causal inference across multiple indicators, with steps from model specification to evaluation and modification, making it a versatile tool across social, behavioral, and economic research.
This article compiles and explains various common anomaly detection techniques—including distribution‑based, distance‑based, density‑based, clustering, tree‑based, dimensionality‑reduction, classification, and prediction methods—providing theoretical descriptions, algorithmic steps, advantages, limitations, and Python code examples for each approach.
This article explains the concept of indicator dimensionless processing and introduces seven common transformation techniques—including standard sample, ratio, vector normalization, range, and efficacy coefficient methods—to convert raw indicator values into comparable evaluation scores.
The article outlines a systematic framework for detecting abnormal KPI fluctuations in daily dashboards—verifying data accuracy, applying period‑over‑period and 3‑sigma rules, then attributing causes across product, competitor and market scopes using MECE‑based horizontal, vertical funnel, and cross analyses, and quantifying impacts with control‑variable, slot, marginal‑effect, prior‑judgment and difference‑in‑differences methods for rapid analyst response and potential automation.
This article explains the concept, history, and various forms of online experiments such as AB testing, ABn, AA, and multivariate tests, highlighting their role in causal inference, value evaluation, risk control, and product optimization within modern internet businesses.
To verify that a probabilistic interface behaves as configured, the article explains how to compute binomial confidence intervals using the normal approximation for moderate probabilities and large samples, or the exact Clopper‑Pearson method for extreme or small samples, and provides Java examples and practical guidelines.
This comprehensive guide explores anomaly (outlier) detection across diverse methods—including time‑series analysis, statistical tests, distance metrics, matrix factorization, graph approaches, behavior‑sequence modeling, and supervised machine‑learning models—highlighting their principles, formulas, and practical use cases such as fraud prevention and system monitoring.
This article explains a six‑layer data analysis framework—from raw data sources and data warehouses through exploration, mining, and visualization—while also reviewing common analysis tools such as R, SAS, SPSS, and describing typical statistical techniques and presentation methods.
This article introduces the Monte Carlo method and demonstrates its versatility through five examples covering π estimation, integral calculation, traffic‑jam simulation, product thickness reliability, and securities market profit forecasting, highlighting its simplicity, power, and broad applicability.
