Tag

statistical inference

0 views collected around this technical thread.

Model Perspective
Model Perspective
Nov 17, 2022 · Fundamentals

Can a Stock’s Volatility Remain Unchanged? Applying the Chi‑Square Test for Variance

This article explains how to use a chi‑square test to assess whether a population variance, such as a stock’s monthly return standard deviation, equals a specified constant, detailing hypothesis formulation, test statistic calculation, critical value lookup, and interpretation of results.

chi-square testsignificance testingstatistical inference
0 likes · 3 min read
Can a Stock’s Volatility Remain Unchanged? Applying the Chi‑Square Test for Variance
Alimama Tech
Alimama Tech
Oct 13, 2021 · Artificial Intelligence

Bootstrap Methods for Statistical Inference in AB Testing

The article explains how the non‑parametric Bootstrap resampling method provides a practical, computationally efficient way to perform statistical inference in AB testing—especially with small samples, skewed data, or ratio metrics—by generating confidence intervals and hypothesis tests via repeated sampling, outperforming traditional approaches.

AB testingBootstrapconfidence interval
0 likes · 9 min read
Bootstrap Methods for Statistical Inference in AB Testing
DataFunTalk
DataFunTalk
Jul 16, 2021 · Fundamentals

Online Traffic Splitting AB Testing: Design, Implementation, Evaluation, and Decision

This article provides a comprehensive guide to online traffic‑splitting AB testing, covering experiment design, metric selection, traffic allocation, implementation details, statistical description, inference methods, deep analysis techniques, and how to make data‑driven decisions on rollout or iteration.

AB testingOnline Traffic Splittingdata analysis
0 likes · 22 min read
Online Traffic Splitting AB Testing: Design, Implementation, Evaluation, and Decision
Alimama Tech
Alimama Tech
Jul 14, 2021 · Big Data

A/B Testing Framework for Online Experiments: Design, Implementation, Analysis, and Decision Making

The paper presents a comprehensive A/B testing framework for online experiments that guides practitioners through four stages—designing objectives and metrics, implementing random traffic allocation with robustness checks, evaluating effects using descriptive statistics and hypothesis testing, and making rollout decisions based on multidimensional significance and attribution analyses.

A/B TestingExperimental designdata analysis
0 likes · 22 min read
A/B Testing Framework for Online Experiments: Design, Implementation, Analysis, and Decision Making