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Python Crawling & Data Mining
Python Crawling & Data Mining
Jun 15, 2025 · Fundamentals

Why Sitting at the Back of a Plane Boosts Survival Odds and What Statistics Reveal

Research by aviation writer David Nolan shows rear‑seat passengers have higher crash survival rates, and the article explains how frequency distribution tables, probability calculations, and hypothesis testing can be used to analyze such data, illustrating core statistical concepts with real‑world aviation examples.

air crash analysisfrequency distributionhypothesis testing
0 likes · 9 min read
Why Sitting at the Back of a Plane Boosts Survival Odds and What Statistics Reveal
Meituan Technology Team
Meituan Technology Team
May 22, 2025 · Fundamentals

Unlocking AB Testing: Core Statistical Principles Behind Reliable Experiments

This article explains the statistical foundations of AB testing, covering the Rubin causal model, SUTVA and randomization assumptions, parameter and confidence‑interval estimation, hypothesis‑testing procedures, and essential limit theorems such as the law of large numbers and the central limit theorem.

AB testingcausal inferencehypothesis testing
0 likes · 17 min read
Unlocking AB Testing: Core Statistical Principles Behind Reliable Experiments
Model Perspective
Model Perspective
Aug 13, 2024 · Fundamentals

Can You Test Life’s Assumptions with Statistical and Bayesian Methods?

This article explores how everyday decisions—from buying breakfast to quitting a job—are built on hidden assumptions and shows how statistical hypothesis testing and Bayesian thinking can help you identify, test, and adjust those assumptions for better outcomes.

bayesian thinkingdecision makinghypothesis testing
0 likes · 5 min read
Can You Test Life’s Assumptions with Statistical and Bayesian Methods?
Huolala Tech
Huolala Tech
Feb 2, 2024 · Fundamentals

How the Delta Method Improves AB Test Variance Estimation When Units Differ

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.

AB testingBootstrapDelta Method
0 likes · 8 min read
How the Delta Method Improves AB Test Variance Estimation When Units Differ
Huolala Tech
Huolala Tech
Nov 24, 2023 · Fundamentals

Master AB Testing: Hypothesis Testing and Minimum Sample Size Made Simple

This article explains the statistical foundations of AB experiments—hypothesis testing and minimum sample size calculation—showing how to determine whether observed differences are real, how to control type‑I and type‑II errors, and how to plan experiments with sufficient power.

experiment designhypothesis testingsample size
0 likes · 15 min read
Master AB Testing: Hypothesis Testing and Minimum Sample Size Made Simple
JD.com Experience Design Center
JD.com Experience Design Center
Jul 5, 2023 · Product Management

How Causal Inference Can Unlock High‑Impact Product Requirements

This article reviews a product‑manager’s end‑to‑end workflow for forecasting demand value and validating hypotheses, illustrating how Wallace’s scientific loop translates to business, and detailing causal‑inference techniques such as matching, DID, regression discontinuity, and instrumental variables with a real‑world case study.

causal inferencedata analysiseconometrics
0 likes · 17 min read
How Causal Inference Can Unlock High‑Impact Product Requirements
Zhuanzhuan Tech
Zhuanzhuan Tech
Mar 8, 2023 · Product Management

A Comprehensive Guide to A/B Testing: Principles, Design, Metrics, and Decision Making

This article explains the fundamentals of A/B testing, why it is essential for data‑driven product decisions, how to design and run experiments—including hypothesis formulation, metric selection, sample size calculation, traffic segmentation, and duration planning—and how to analyze results using T‑tests, P‑values, and structured decision processes.

A/B testingMetricsdecision making
0 likes · 15 min read
A Comprehensive Guide to A/B Testing: Principles, Design, Metrics, and Decision Making
Model Perspective
Model Perspective
Nov 30, 2022 · Fundamentals

How to Perform One‑Sample and Two‑Sample t‑Tests in R

This article explains the theory behind hypothesis testing, demonstrates one‑sample and two‑sample t‑tests using R’s t.test() function with agricultural yield examples, and shows how to interpret one‑sided versus two‑sided test results for decision making.

Rdata analysishypothesis testing
0 likes · 5 min read
How to Perform One‑Sample and Two‑Sample t‑Tests in R
Model Perspective
Model Perspective
Nov 23, 2022 · Fundamentals

Why Power Analysis Is Essential for Reliable Research Results

This article explains the importance of power analysis in hypothesis testing, illustrates how insufficient sample size can mask true effects, demonstrates calculating required sample sizes for desired power, and highlights the trade‑off between Type I and Type II errors in experimental design.

hypothesis testingpower analysisresearch methods
0 likes · 4 min read
Why Power Analysis Is Essential for Reliable Research Results
Model Perspective
Model Perspective
Nov 18, 2022 · Fundamentals

How to Use an F-Test to Compare Stock Return Variances: A Step-by-Step Example

This article walks through a complete F‑test example comparing the monthly return variances of IBM and HP stocks over 36 months, detailing hypothesis formulation, calculation of the test statistic, critical value lookup, and interpretation of results to determine if the variances differ significantly.

F-testfinancial analysishypothesis testing
0 likes · 2 min read
How to Use an F-Test to Compare Stock Return Variances: A Step-by-Step Example
Model Perspective
Model Perspective
Nov 17, 2022 · Fundamentals

How to Use an F-Test to Compare Stock Return Variances: A Step-by-Step Guide

This article explains the null and alternative hypotheses for one‑ and two‑tailed variance tests, demonstrates how to compute the F‑statistic and critical value, and walks through a practical example comparing the monthly return standard deviations of IBM and HP stocks, concluding that their variances are not significantly different.

F-testhypothesis testingstatistics
0 likes · 2 min read
How to Use an F-Test to Compare Stock Return Variances: A Step-by-Step Guide
Model Perspective
Model Perspective
Nov 15, 2022 · Fundamentals

How to Use Paired Comparison Tests for Evaluating Investment Strategies

This article explains the paired comparison test for non‑independent samples, outlines the null and alternative hypotheses, presents the t‑test statistic formula, and walks through a detailed example evaluating a Dow‑10 investment strategy versus a buy‑and‑hold approach.

financehypothesis testinginvestment strategy
0 likes · 4 min read
How to Use Paired Comparison Tests for Evaluating Investment Strategies
Model Perspective
Model Perspective
Nov 14, 2022 · Fundamentals

Testing Equality of Means for Two Independent Normal Populations

This article explains how to conduct hypothesis tests for equality of means between two independent normal populations, covering both equal‑variance and unequal‑variance cases, detailing test statistics, degrees of freedom, critical values, and decision rules through examples on S&P 500 returns and bond recovery rates.

hypothesis testingindependent samplesstatistics
0 likes · 6 min read
Testing Equality of Means for Two Independent Normal Populations
Model Perspective
Model Perspective
Nov 12, 2022 · Fundamentals

How to Perform One-Way ANOVA in Python: Theory, Example, and Code

This article explains the concept of one‑factor (one‑way) ANOVA, walks through a lamp‑life example with four manufacturing processes, derives the within‑ and between‑group sum‑of‑squares formulas, and shows how to execute the test in Python using statsmodels.

ANOVAPythonhypothesis testing
0 likes · 6 min read
How to Perform One-Way ANOVA in Python: Theory, Example, and Code
Model Perspective
Model Perspective
Nov 12, 2022 · Fundamentals

Mastering Hypothesis Testing: Core Concepts, Steps, and Common Errors

This article explains the fundamentals of hypothesis testing, covering its definition, significance testing, underlying small‑probability principle, test statistics, rejection and acceptance regions for one‑ and two‑tailed tests, step‑by‑step procedures, and the two types of errors involved.

hypothesis testingsignificance levelstatistics
0 likes · 9 min read
Mastering Hypothesis Testing: Core Concepts, Steps, and Common Errors
Model Perspective
Model Perspective
Sep 11, 2022 · Fundamentals

The Hidden Danger of Misunderstanding Moivre’s Formula in Educational Data

This article explains how overlooking Moivre’s formula can lead to misleading conclusions about school performance, demonstrates the role of sample size, standard error, confidence intervals, and hypothesis testing using real ENEM data, and clarifies common misconceptions about p‑values and confidence levels.

Moivre's formulaconfidence intervalhypothesis testing
0 likes · 15 min read
The Hidden Danger of Misunderstanding Moivre’s Formula in Educational Data
Model Perspective
Model Perspective
Sep 10, 2022 · Fundamentals

What Is Statistics? A Beginner’s Guide to Data Collection, Analysis, and Inference

This article introduces the fundamentals of statistics, covering its purpose, types, data collection methods, data organization steps, graphical representation, measures of central tendency and dispersion, probability concepts, parameter estimation, hypothesis testing, and the distinction between correlation and regression analysis.

data analysishypothesis testingprobability
0 likes · 11 min read
What Is Statistics? A Beginner’s Guide to Data Collection, Analysis, and Inference
ByteDance Data Platform
ByteDance Data Platform
Sep 7, 2022 · Product Management

How to Calculate Minimum Sample Size for Reliable A/B Tests

This article explains common pain points in A/B testing, introduces essential statistical concepts such as sampling distribution, parameter estimation, confidence intervals, and hypothesis testing, and provides step‑by‑step formulas and a concrete example for calculating the minimum sample size needed to run a trustworthy experiment.

A/B testinghypothesis testingproduct experimentation
0 likes · 14 min read
How to Calculate Minimum Sample Size for Reliable A/B Tests
DeWu Technology
DeWu Technology
Aug 29, 2022 · Fundamentals

Fundamentals of Statistics for A/B Testing and Its Application in the DeWu Platform

A solid grasp of basic statistical concepts—such as populations, samples, means, variance, probability distributions, the Central Limit Theorem, and hypothesis testing—enables designers of A/B experiments to correctly size samples, interpret p‑values and confidence intervals, and reliably deploy DeWu’s integrated platform for automated experiment allocation, metric monitoring, and one‑click reporting, ultimately driving data‑driven product decisions.

A/B testingData-drivenconfidence interval
0 likes · 13 min read
Fundamentals of Statistics for A/B Testing and Its Application in the DeWu Platform
Model Perspective
Model Perspective
Aug 16, 2022 · Fundamentals

13 Essential Statistical Analysis Methods Every Researcher Should Know

This article outlines thirteen key statistical techniques—including descriptive and inferential methods, hypothesis testing, reliability analysis, contingency tables, regression, clustering, discriminant, factor, and time‑series analysis—explaining their purposes, assumptions, and typical applications for researchers and data analysts.

clusteringdata analysishypothesis testing
0 likes · 25 min read
13 Essential Statistical Analysis Methods Every Researcher Should Know
Model Perspective
Model Perspective
Jul 12, 2022 · Fundamentals

How Simple Linear Regression Uncovers Hidden Relationships in Data

This article explains the theory and practice of simple linear regression, covering deterministic vs. stochastic relationships, the least‑squares estimation of coefficients, goodness‑of‑fit measures such as R², hypothesis testing for linearity, and a real‑world case linking wine consumption to heart‑disease mortality.

Least SquaresR-squaredStatistical Modeling
0 likes · 8 min read
How Simple Linear Regression Uncovers Hidden Relationships in Data
Model Perspective
Model Perspective
Jul 11, 2022 · Fundamentals

How to Perform One-Way ANOVA in Python: A Step-by-Step Guide

This article explains the concept of one‑way ANOVA, walks through a real‑world example comparing four manufacturing processes, and demonstrates how to conduct the analysis in Python using statsmodels, interpreting the resulting F‑statistic and p‑value to assess significance.

ANOVAPythonStatsmodels
0 likes · 6 min read
How to Perform One-Way ANOVA in Python: A Step-by-Step Guide
Model Perspective
Model Perspective
Jul 6, 2022 · Fundamentals

Testing Proportions with Large Samples: Hypotheses, CI & Critical Values

This article explains how to conduct large‑sample hypothesis tests for a population proportion, covering the formulation of null and alternative hypotheses, constructing 99% confidence intervals using the Central Limit Theorem, calculating standardized test statistics, determining rejection regions, and applying the method to a real‑world soft‑drink preference case study.

confidence intervalhypothesis testinglarge sample
0 likes · 4 min read
Testing Proportions with Large Samples: Hypotheses, CI & Critical Values
vivo Internet Technology
vivo Internet Technology
Jun 29, 2022 · Operations

Intelligent Gray Release Data Analysis System Practice for Game Center

The article details vivo Game Center’s end‑to‑end intelligent gray‑release data analysis system, which combines experimental design, statistical significance testing, multi‑dimensional anomaly root‑cause analysis (via the Adtributor algorithm), automated reporting, and sample‑size estimation to enable scientific version evaluation, rapid issue detection, and a fast closed‑loop verification process for game updates.

Vivoab-testingadtributor
0 likes · 14 min read
Intelligent Gray Release Data Analysis System Practice for Game Center
Model Perspective
Model Perspective
Jun 8, 2022 · Fundamentals

Understanding Hypothesis Testing: Core Concepts, Steps, and Common Errors

This article explains the fundamentals of hypothesis testing, covering its definition, significance testing, underlying small‑probability principle, test statistics, rejection and acceptance regions, step‑by‑step procedures, and the two types of errors, providing a concise overview for statistical inference.

hypothesis testingsignificance levelstatistics
0 likes · 9 min read
Understanding Hypothesis Testing: Core Concepts, Steps, and Common Errors
Python Programming Learning Circle
Python Programming Learning Circle
Apr 12, 2022 · Fundamentals

Implementing Common Hypothesis Tests in Python: Z‑test, t‑test, F‑test and Data Exploration

This article demonstrates how to conduct various hypothesis tests—including Z‑test, one‑sample and two‑sample t‑tests, F‑test, as well as distribution fitting and outlier detection—using Python libraries such as Statsmodels, SciPy, and pandas on both the Iris dataset and a human temperature dataset.

PythonZ-testdata analysis
0 likes · 9 min read
Implementing Common Hypothesis Tests in Python: Z‑test, t‑test, F‑test and Data Exploration
TAL Education Technology
TAL Education Technology
Aug 12, 2021 · Fundamentals

Statistical Foundations and Practical Applications of A/B Testing

This article explains the statistical principles behind A/B testing, covering concepts such as populations, samples, parameters, hypothesis testing, significance levels, t‑tests, metric types, p‑value calculations, and real‑world examples to guide data‑driven product decisions.

A/B testingMetric Evaluationexperiment design
0 likes · 12 min read
Statistical Foundations and Practical Applications of A/B Testing
Xueersi Online School Tech Team
Xueersi Online School Tech Team
Feb 14, 2020 · Product Management

Understanding A/B Testing: Statistical Foundations, Metric Evaluation, and Practical Applications

This article explains the principles of A/B testing, the statistical concepts such as population, sample, hypothesis testing, p‑values and t‑tests, describes how to calculate metrics for rate and mean indicators, and illustrates a real‑world experiment with detailed evaluation methods.

A/B testingexperiment designhypothesis testing
0 likes · 14 min read
Understanding A/B Testing: Statistical Foundations, Metric Evaluation, and Practical Applications
58UXD
58UXD
May 28, 2019 · Product Management

How to Use Lean User Research and MVPs to Confirm If Users Really Want Your Product

This article explains practical lean user‑research methods and step‑by‑step MVP creation techniques that help product teams validate assumptions, discover genuine user demand, and reduce development risk by efficiently testing whether users truly want a product.

MVPhypothesis testinglean user research
0 likes · 7 min read
How to Use Lean User Research and MVPs to Confirm If Users Really Want Your Product
21CTO
21CTO
Sep 27, 2017 · Fundamentals

Mastering A/B Testing: Essential Statistical Concepts for Data‑Driven Decisions

This article explains the statistical foundations of A/B experiments—including population, sample, sampling error, confidence intervals, hypothesis testing, type I/II errors, statistical significance, and power—so engineers can design reliable tests and interpret results with confidence.

A/B testingconfidence intervalhypothesis testing
0 likes · 20 min read
Mastering A/B Testing: Essential Statistical Concepts for Data‑Driven Decisions
Didi Tech
Didi Tech
Aug 10, 2017 · Fundamentals

Understanding Hypothesis Testing and Statistical Significance in A/B Experiments

The article explains hypothesis testing in A/B experiments, describing null and alternative hypotheses, type I and II errors, p‑values, statistical significance versus practical impact, confidence intervals, statistical power, sample‑size planning, and a checklist for interpreting results responsibly.

A/B testingconfidence intervalhypothesis testing
0 likes · 15 min read
Understanding Hypothesis Testing and Statistical Significance in A/B Experiments