statistical analysis

Python Programming Learning Circle

Jun 13, 2025 · Fundamentals

Analyzing 2013 Toulouse Airport Weather Data with Python, pandas, and SciPy

This tutorial demonstrates how to import, clean, and explore 2013 weather observations from Toulouse Airport using Python libraries such as pandas and SciPy, perform consistency checks, visualize temperature trends, assess variable correlations, and fit probability distributions—including normal, log‑normal, and Weibull—to the data.

PythonSciPydistribution fitting

0 likes · 7 min read

Analyzing 2013 Toulouse Airport Weather Data with Python, pandas, and SciPy

Didi Tech

Apr 10, 2025 · Product Management

AA Testing and Rerandomization Techniques for Reliable AB Experiments

The article outlines how AA testing and rerandomization can detect and correct non‑uniform traffic splits in short‑term AB experiments, detailing three solutions—AA tests, seed‑based rerandomization, and retrospective AA analysis—along with theoretical guarantees, empirical error‑rate reductions, and remaining challenges for long‑term or clustered designs.

AA testingAB testingCUPED

0 likes · 17 min read

AA Testing and Rerandomization Techniques for Reliable AB Experiments

DataFunTalk

May 23, 2024 · Fundamentals

Systematic Solutions to the AA Problem in Random Experiments

Speaker Wanbo Kui, a Didi data analyst, will present a systematic approach to addressing the AA problem in random experiments, covering academic and industry research on re-randomization, its principles and simulations, practical applications, and how it enhances experiment validity.

A/B testingAA problemexperiment design

0 likes · 3 min read

Systematic Solutions to the AA Problem in Random Experiments

DaTaobao Tech

Feb 24, 2023 · Artificial Intelligence

Data Preprocessing and Statistical Analysis Techniques in Python

The article reviews essential Python data‑preprocessing and statistical‑analysis tools—including missing‑value imputation, outlier trimming, scaling, binning, knee‑point detection, correlation, chi‑square testing, linear regression, Wilson scoring, PCA weighting, text tokenization and sentiment analysis, plus visualization with matplotlib/seaborn and big‑data access via pyodps.

Pythonbig datadata preprocessing

0 likes · 17 min read

Data Preprocessing and Statistical Analysis Techniques in Python

Dada Group Technology

Dec 30, 2022 · Fundamentals

Ensuring Trustworthy A/B Experiments: Architecture, Balance Checks, Log Consistency, Automated Significance Testing, and Result Interpretation

This article discusses how to improve the reliability of online A/B experiments by designing robust architecture, evaluating group balance with orthogonal testing, ensuring consistent front‑end/back‑end logging, automating statistical significance checks, reducing group imbalance, and interpreting results using causal trees.

A/B testingcausal treesdata collection

0 likes · 12 min read

Ensuring Trustworthy A/B Experiments: Architecture, Balance Checks, Log Consistency, Automated Significance Testing, and Result Interpretation

Model Perspective

Dec 10, 2022 · Fundamentals

How to Perform One-Way ANOVA in SPSS: Step‑by‑Step Guide with Real Data

This article explains how to use SPSS to conduct a one‑way ANOVA, walks through a clinical case with ALT measurements, shows the required SPSS settings, interprets descriptive and post‑hoc results, and provides guidance for writing the final statistical conclusion.

ALT levelsPost-hoc testsSPSS

0 likes · 10 min read

How to Perform One-Way ANOVA in SPSS: Step‑by‑Step Guide with Real Data

Model Perspective

Dec 8, 2022 · Fundamentals

Can Keratin‑10 Improve Hemoglobin in Silicosis? A Paired t‑Test Walkthrough in SPSS

This article guides readers through a paired‑samples t‑test in SPSS to evaluate whether Keratin‑10 treatment significantly changes hemoglobin levels in ten silicosis patients, covering data preparation, test execution, result interpretation, and concluding that the drug shows no statistical effect.

SPSShemoglobinmedical statistics

0 likes · 4 min read

Can Keratin‑10 Improve Hemoglobin in Silicosis? A Paired t‑Test Walkthrough in SPSS

Model Perspective

Dec 1, 2022 · Fundamentals

Mastering One‑Way and Multi‑Factor ANOVA in R: From Theory to Code

This article explains the principles of one‑way, two‑way and multi‑factor ANOVA, demonstrates how to perform them in R using aov(), lm() and anova(), and shows post‑hoc comparisons with TukeyHSD as well as correlation testing with cor() and cor.test().

ANOVARTukey test

0 likes · 10 min read

Mastering One‑Way and Multi‑Factor ANOVA in R: From Theory to Code

DataFunTalk

Nov 30, 2022 · Big Data

Design and Practice of Yanxuan A/B Scientific Experiment Platform

The article presents the design, scientific methodology, system architecture, and case studies of Yanxuan's A/B testing platform, detailing how statistical principles, automated tracking, traffic allocation models, and unified reporting accelerate decision‑making and reduce development effort in e‑commerce experiments.

A/B testingautomationdata pipeline

0 likes · 15 min read

Design and Practice of Yanxuan A/B Scientific Experiment Platform

DataFunTalk

Nov 25, 2022 · Operations

Overview of Volcano Engine A/B Experiment System Platform

This article presents a comprehensive overview of Volcano Engine's A/B testing platform, detailing its four core stages—reliable experiment system, efficient data construction, scientific statistical analysis, and fine-grained governance—while explaining execution components, data pipelines, statistical methods, and operational best practices for large‑scale experimentation.

A/B testingbig datadata pipeline

0 likes · 16 min read

Overview of Volcano Engine A/B Experiment System Platform

Model Perspective

Sep 9, 2022 · Fundamentals

What Is a Time Series and How Do We Analyze Its Patterns?

A time series is a chronologically ordered set of interrelated data points whose analysis involves studying its development patterns and forecasting future behavior, with classifications based on dimensionality, continuity, statistical properties such as stationarity, and distribution types like Gaussian or non‑Gaussian.

forecastingmultivariatestationarity

0 likes · 2 min read

What Is a Time Series and How Do We Analyze Its Patterns?

Model Perspective

Jul 15, 2022 · Fundamentals

How to Perform Two-Way ANOVA with Python’s statsmodels: Theory and Code

This article explains the theory behind two‑factor ANOVA, distinguishes cases with and without interaction, presents the mathematical model, and demonstrates a complete Python implementation using statsmodels, including data setup, model fitting, and interpretation of the ANOVA table.

Experimental designPythonstatistical analysis

0 likes · 6 min read

How to Perform Two-Way ANOVA with Python’s statsmodels: Theory and Code

Model Perspective

Jun 4, 2022 · Fundamentals

Master Variable Clustering: Measuring Similarity and Grouping Techniques

This article explains the variable clustering method, why it’s needed to reduce redundant variables, how to measure similarity using correlation coefficients or cosine angles, and describes common distance definitions such as maximum and minimum coefficient methods for effective factor selection.

data modelingfactor selectionsimilarity measures

0 likes · 3 min read

Master Variable Clustering: Measuring Similarity and Grouping Techniques

HomeTech

Mar 24, 2022 · Fundamentals

A/B Testing Platform Overview and Statistical Evaluation Methods

This article introduces the A/B testing platform used at AutoHome, detailing its architecture, experiment flow, traffic allocation strategies, and statistical evaluation techniques such as hypothesis testing, confidence intervals, and significance testing, to guide data‑driven decision making for recommendation system improvements.

A/B testingRecommendation systemsdata-driven decisions

0 likes · 9 min read

A/B Testing Platform Overview and Statistical Evaluation Methods

DevOps

Feb 24, 2022 · Product Management

A/B Testing: Motivation, Architecture, Best Practices, and Future Outlook

This article explains why A/B testing is essential for data‑driven decision making, describes the Volcano Engine A/B testing system architecture, outlines practical experiment design, statistical analysis methods, real‑world case studies, and forecasts industry and technical trends for the practice.

A/B testingdata-driven decisionexperiment design

0 likes · 15 min read

A/B Testing: Motivation, Architecture, Best Practices, and Future Outlook

NetEase LeiHuo Testing Center

Dec 6, 2021 · Game Development

How to Verify the Correctness of Probabilities in Game QA

This article explains how QA engineers can rigorously test and validate in‑game probability settings by collecting large samples, analyzing distribution patterns, and using statistical visualizations to ensure random mechanics behave as intended.

distributiongame QAgame development

0 likes · 8 min read

How to Verify the Correctness of Probabilities in Game QA

Alimama Tech

Nov 3, 2021 · Product Management

Common Pitfalls in AB Testing: Design and Analysis Issues

AB testing often fails because practitioners skip power analysis, peek at interim results, set unrealistic null hypotheses, randomize at inappropriate units, ignore sample‑ratio mismatches, choose misleading metrics, and fall prey to segmentation errors like Simpson’s paradox, any of which can invalidate conclusions.

AB testingMetricsSample Ratio Mismatch

0 likes · 15 min read

Common Pitfalls in AB Testing: Design and Analysis Issues

iQIYI Technical Product Team

Aug 27, 2021 · Backend Development

iQIYI AB Testing Platform: Architecture, Workflow, and Statistical Practices

iQIYI’s AB testing platform integrates a layered traffic‑splitting architecture, real‑time SDK and API delivery, log‑replay data collection, and rigorous T‑test statistical analysis to enable fast, reliable product, algorithm, and operations experiments, exemplified by a UI redesign that boosted watch time by 17.85%.

AB testingTraffic Splittingexperiment platform

0 likes · 12 min read

iQIYI AB Testing Platform: Architecture, Workflow, and Statistical Practices

DataFunTalk

Mar 18, 2021 · Fundamentals

Building Popper: Tubi’s Scalable Experimentation Platform

Tubi’s Popper platform combines a Scala‑based experiment engine, reproducible JSON‑stored configurations, a React UI, and data pipelines using Spark and Akka to enable fast, cross‑team A/B testing, automated analysis, health checks, and data‑driven decision making across mobile and OTT services.

A/B testingAkkaExperimentation platform

0 likes · 15 min read

Building Popper: Tubi’s Scalable Experimentation Platform

Efficient Ops

Feb 1, 2021 · Operations

How to Detect Anomalous Nodes in Massive Compute Clusters Using Intelligent Ops

This article explains how internet companies can reduce soaring manual operations costs by applying intelligent monitoring techniques—such as pattern recognition and statistical anomaly detection—to automatically identify abnormal nodes among thousands of servers, streamline fault diagnosis, and improve service quality.

Anomaly Detectionlarge-scale systemsmonitoring

0 likes · 4 min read

How to Detect Anomalous Nodes in Massive Compute Clusters Using Intelligent Ops