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hypothesis testing

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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?
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 testingdecision makingexperiment design
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 20, 2022 · Fundamentals

How Simple Linear Regression Predicts Outcomes: Model, Assumptions, and Evaluation

This article explains the simple linear regression model, its six key assumptions, how to evaluate the fit using ANOVA and the coefficient of determination, and covers hypothesis testing and confidence intervals for regression coefficients with practical examples.

ANOVAconfidence intervalhypothesis testing
0 likes · 9 min read
How Simple Linear Regression Predicts Outcomes: Model, Assumptions, and Evaluation
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 testingconfidence intervaldata-driven
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 20, 2022 · Fundamentals

Unlocking Multiple Linear Regression: Theory, Estimation, and Prediction

This article explains the fundamentals of multiple linear regression, covering model formulation, least‑squares estimation of coefficients, hypothesis testing of the regression equation, and how to use the fitted model for point and interval predictions.

hypothesis testingleast squaresmultiple regression
0 likes · 5 min read
Unlocking Multiple Linear Regression: Theory, Estimation, and Prediction
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.

R-squaredhypothesis testingleast squares
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.

ANOVAPythonhypothesis testing
0 likes · 6 min read
How to Perform One-Way ANOVA in Python: A Step-by-Step Guide
Model Perspective
Model Perspective
Jul 9, 2022 · Fundamentals

Unlocking Multiple Linear Regression: Theory, Estimation, and Prediction

This article explains the fundamentals of multiple linear regression, covering model formulation, least‑squares estimation of coefficients, statistical tests for significance, and how to use the fitted equation for point and interval predictions.

hypothesis testingleast squaresmultiple regression
0 likes · 4 min read
Unlocking Multiple Linear Regression: Theory, Estimation, and Prediction