statistical methods

JD Tech Talk

Jun 12, 2025 · Product Management

How to Tackle Outliers in Internet A/B Experiments: Methods, Pitfalls, and Practical Tips

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.

A/B testingexperiment designoutlier detection

0 likes · 15 min read

How to Tackle Outliers in Internet A/B Experiments: Methods, Pitfalls, and Practical Tips

Python Programming Learning Circle

Jun 4, 2025 · Fundamentals

Implementing Geodetector in Python with the py_geodetector Library

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.

GeodetectorSpatial Analysispy_geodetector

0 likes · 4 min read

Implementing Geodetector in Python with the py_geodetector Library

JD Tech

Jan 13, 2025 · Fundamentals

Handling Outliers in Internet A/B Experiments: Concepts, Methods, and Practical Recommendations

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.

A/B testingTRIMdata analysis

0 likes · 8 min read

Handling Outliers in Internet A/B Experiments: Concepts, Methods, and Practical Recommendations

JD Retail Technology

Jan 7, 2025 · Fundamentals

Handling Outliers in Internet A/B Experiments: Concepts, Methods, and Practical Recommendations

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.

A/B testingBig DataTRIM

0 likes · 10 min read

DataFunSummit

Aug 18, 2024 · Artificial Intelligence

Challenges and Solutions in Recommendation AB Testing on Xiaohongshu's Experiment Platform

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.

AB testingCUPEDPreAA

0 likes · 15 min read

Challenges and Solutions in Recommendation AB Testing on Xiaohongshu's Experiment Platform

Model Perspective

Apr 18, 2024 · Fundamentals

How Structural Equation Modeling Reveals Hidden Causal Links

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.

Latent VariablesStructural Equation Modelingcausal inference

0 likes · 9 min read

How Structural Equation Modeling Reveals Hidden Causal Links

Test Development Learning Exchange

Jan 18, 2024 · Fundamentals

Common Statistical Methods for Data Analysis with Python Code Examples

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.

data analysismachine learningstatistical methods

0 likes · 7 min read

Common Statistical Methods for Data Analysis with Python Code Examples

DataFunSummit

Dec 19, 2023 · Big Data

Metric Anomaly Detection and Diagnosis Practices at NetEase Yanxuan

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.

Big DataDiagnosticscontribution analysis

0 likes · 18 min read

Metric Anomaly Detection and Diagnosis Practices at NetEase Yanxuan

DataFunTalk

Nov 8, 2023 · Fundamentals

Metric Anomaly Detection and Diagnosis Practices at NetEase Yanxuan

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.

contribution decompositiondiagnostic analysise-commerce analytics

0 likes · 17 min read

Model Perspective

Sep 6, 2023 · Fundamentals

How Box‑Cox Transformation Turns Skewed Data Into Normal Distributions

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.

Box-CoxData TransformationNormality

0 likes · 8 min read

How Box‑Cox Transformation Turns Skewed Data Into Normal Distributions

Model Perspective

Feb 27, 2023 · Fundamentals

Mastering Difference-in-Differences: Theory, Example, and Python Implementation

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.

Difference-in-DifferencesPolicy EvaluationPython

0 likes · 10 min read

Mastering Difference-in-Differences: Theory, Example, and Python Implementation

Zhuanzhuan Tech

Dec 28, 2022 · Product Management

A Comprehensive Guide to A/B Testing: System Design, Implementation, and Best Practices

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.

A/B testingdata analysisexperiment design

0 likes · 20 min read

A Comprehensive Guide to A/B Testing: System Design, Implementation, and Best Practices

Model Perspective

Nov 17, 2022 · Artificial Intelligence

How Mathematics Sparked the Rise of Modern Linguistics and NLP

This article traces the historical convergence of mathematics and linguistics, from 19th‑century pioneers to post‑war computer‑driven research, highlighting how statistical, probabilistic, and formal methods laid the foundation for machine translation, morphological analysis, and contemporary natural language processing.

Natural Language Processinghistory of linguisticsmachine translation

0 likes · 7 min read

How Mathematics Sparked the Rise of Modern Linguistics and NLP

Model Perspective

Sep 17, 2022 · Fundamentals

Unlocking Insights with Structural Equation Modeling: A Practical Guide

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.

Latent VariablesSEMStructural Equation Modeling

0 likes · 8 min read

Unlocking Insights with Structural Equation Modeling: A Practical Guide

Python Programming Learning Circle

Jul 15, 2022 · Artificial Intelligence

Comprehensive Overview of Common Anomaly Detection Methods with Python Code Examples

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.

Anomaly DetectionPythonmachine learning

0 likes · 18 min read

Comprehensive Overview of Common Anomaly Detection Methods with Python Code Examples

Model Perspective

May 15, 2022 · Fundamentals

How to Normalize Indicators: 7 Essential Dimensionless Transformation Methods

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.

data normalizationdata preprocessingdimensionless scaling

0 likes · 3 min read

How to Normalize Indicators: 7 Essential Dimensionless Transformation Methods

Xianyu Technology

Sep 7, 2021 · Big Data

Analyzing Business Data Fluctuations and Attribution Methods

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.

AttributionBusiness IntelligenceKPI monitoring

0 likes · 7 min read

Analyzing Business Data Fluctuations and Attribution Methods

DataFunTalk

Jul 11, 2021 · Fundamentals

Understanding Online Experiments: Origins, Types, and Applications

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.

AB testingcausal inferenceexperiment design

0 likes · 16 min read

Understanding Online Experiments: Origins, Types, and Applications

DeWu Technology

Dec 25, 2020 · Fundamentals

Testing Probabilistic Events with Binomial Confidence Intervals

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.

Clopper-PearsonJava testingbinomial confidence interval

0 likes · 9 min read

Testing Probabilistic Events with Binomial Confidence Intervals

Architects' Tech Alliance

Mar 17, 2017 · Big Data

Multi‑Layer Data Analysis Model, Tools, and Common Statistical Methods

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.

Big DataData MiningData Visualization

0 likes · 7 min read

Multi‑Layer Data Analysis Model, Tools, and Common Statistical Methods