KA114001 Statistical Test

Hiroshima University Syllabus

Japanese

Academic Year 2026Year School/Graduate School School of Informatics and Data Science

Lecture Code KA114001 Subject Classification Specialized Education

Subject Name 統計的検定

Subject Name
（Katakana）トウケイテキケンテイ

Subject Name in
English Statistical Test

Instructor LOU YANG

Instructor
(Katakana) ロウ　ヤン

Campus Higashi-Hiroshima Semester/Term 2nd-Year,  First Semester,  2Term

Days, Periods, and Classrooms (2T) Tues5-8：IAS K108

Lesson Style Lecture Lesson Style
(More Details) Face-to-face, Online (on-demand)

This is a lecture-based class, primarily held in person, with online (on demand) sessions available only in special situations, such as severe weather, the instructor’s business trip, or other exceptional circumstances.
The first lesson will be conducted in person.

Credits 2.0 Class Hours/Week 4 Language of Instruction E : English

Course Level 2 : Undergraduate Low-Intermediate

Course Area（Area） 25 : Science and Technology

Course Area（Discipline） 01 : Mathematics/Statistics

Eligible Students

Keywords Hypothesis Testing; Sampling and Resampling; Test Statistics and Distributions; Statistical Significance

Special Subject for Teacher Education 　 Special Subject 　

Class Status
within Educational
Program
(Applicable only to targeted subjects for undergraduate students)
Criterion referenced
Evaluation
(Applicable only to targeted subjects for undergraduate students) Computer Science Program
（Abilities and Skills）
・A. Information infrastructure development technology, information processing technology, technology that analyzes data and creates new added value.
・B. Ability to identify new problems independently and solve them through quantitative and logical thinking based on data, multifaceted perspectives, and advanced information processing and analysis.

Data Science Program
（Knowledge and Understanding）
・D1. Knowledge and ability to understand the theoretical framework of statistics and data analysis and to analyze qualitative/quantitative information of big data accurately and efficiently.
（Abilities and Skills）
・A. Information infrastructure development technology, information processing technology, technology that analyzes data and creates new added value.
・B. Ability to identify new problems independently and solve them through quantitative and logical thinking based on data, multifaceted perspectives, and advanced information processing and analysis.

Intelligence Science Program
（Knowledge and Understanding）
・D1. A deep systematic understanding of the advanced intelligence of human beings and its realization by computers.
（Abilities and Skills）
・A. Information infrastructure development technology, information processing technology, technology that analyzes data and creates new added value.
・B. Ability to identify new problems independently and solve them through quantitative and logical thinking based on data, multifaceted perspectives, and advanced information processing and analysis.

Class Objectives
/Class Outline Understand and apply statistical hypothesis testing, and develop foundational statistical reasoning skills for data science.

Class Schedule lesson1 Data, Location, and Variability as the Basis of Statistical
  o COURSE INTRODUCTION
  o (Chapter 1: Exploratory Data Analysis)
  o Exploring Data Analysis
  o Rectangular Data
  o Estimates of Location
  o Variability Metrics and Variability Estimates
lesson2 Distributional Shape and Association for Exploratory Testing
  o Exploring the Data Distribution
  o Percentiles and Boxplots
  o Frequency Tables and Histograms
  o Density Plot
  o Exploring Binary and Categorical Data
  o Mode, Probability & Expected Value
  o Correlation
  o Scatterplots
lesson3 Multivariate Structure and Visualization for Statistical Comparison
  o Exploring Two or More Variables
  o Hexagonal Binning & Contour Plot
  o Two Categorical Variables
  o Categorical and Numeric Data
  o Visualizing Multiple Variables
lesson4 Random Sampling and Sampling Distributions
  o (Chapter 2: Data and Sampling Distributions)
  o Random Sampling and Sample Bias
  o Sample Mean vs. Population Mean
  o Regression to the Mean
  o Sampling Distribution
lesson5 Central Limit Theorem and Standard Error
  o Central Limit Theorem
  o Standard Error
lesson6 Resampling, Bootstrap, and Distributional Assumptions
  o Bootstrap
  o Resampling vs. Bootstrapping
  o Confidence Interval Concept
  o Normal Distribution
  o Standard Normal and QQ-Plots
  o Long-Tailed Distributions
  o Student's t-Distribution
lesson7 Confidence Intervals and Discrete Distributions in Testing
  o Confidence Interval Formula
  o Binomial Distribution
  o Chi-Square Distribution
  o F-Distribution
  o Poisson Distributions
lesson8 Continuous Distributions for Statistical Modeling
  o Exponential Distribution
  o Weibull Distribution
lesson9  Hypothesis Testing Logic and Experimental Design
  o (Chapter 3: Statistical Experiments and Significance Testing)
  o Design of Experiments and Statistical Inference
  o A/B Testing
  o Hypothesis Tests
  o Misinterpreting Randomness
  o Null Hypothesis
  o Alternative Hypothesis
  o One-Sided Versus Two-Sided Hypothesis Tests
lesson10 Randomization and Permutation-Based Tests
  o Resampling
  o Permutation Test
  o Exhaustive and Bootstrap Permutation Tests
lesson11 p-Values, Error Types, and Statistical Significance
  o Statistical Significance and p-Values
  o p-Value from Permutation Test
  o Alpha in Statistical Testing
  o ASA (American Statistical Association) Principles on p-Value
  o Type I and Type II Errors
lesson12 Test Statistics and Variance Decomposition
  o t-Tests
  o Multiple Testing
  o Degrees of Freedom
  o ANOVA
  o F-Statistic
  o Decomposition of Variance
  o Two-Way ANOVA Concept
lesson13 Categorical Data Tests and Exact Inference
  o Pearson Residuals
  o Chi-Square Statistic
  o Chi-Square Test: Resampling Algorithm and Statistical Theory
  o Fisher's Exact Test
lesson14 Power, Sample Size, and Sequential Decision Concepts
  o Multi-Arm Bandit Algorithm Concepts
  o Power and Sample Size
  o Power Calculation
lesson15 Course Review and Quiz

Text/Reference
Books,etc. P. Bruce, A. Bruce, and P. Gedeck. Practical Statistics for Data Scientists (Second Edition), O’Reilly Media, 2020.
[Chapters 1–3 only]

PC or AV used in
Class,etc. Text, Handouts, moodle

(More Details)

Learning techniques to be incorporated Quizzes/ Quiz format, Post-class Report

Suggestions on
Preparation and
Review 1. Each lesson's materials (e.g., slides provided by the instructor) should be reviewed both before and after class.
2. The meaning of all English technical terms should be understood before class.

Requirements Prior coursework in inferential statistics is desirable, along with a working background in either R or Python programming.

Grading Method The grade will be based on the following two components:
1. in‑class tests, and
2. a final report.

Practical Experience

Summary of Practical Experience and Class Contents based on it

Message

Other Please bring your own PC, as we will be doing exercises.

Please fill in the class improvement questionnaire which is carried out on all classes.
Instructors will reflect on your feedback and utilize the information for improving their teaching.

Academic Year	2026Year	School/Graduate School	School of Informatics and Data Science
Lecture Code	KA114001	Subject Classification	Specialized Education
Subject Name	統計的検定
Subject Name （Katakana）	トウケイテキケンテイ
Subject Name in English	Statistical Test
Instructor	LOU YANG
Instructor (Katakana)	ロウ　ヤン
Campus	Higashi-Hiroshima	Semester/Term	2nd-Year, First Semester, 2Term
Days, Periods, and Classrooms	(2T) Tues5-8：IAS K108
Lesson Style	Lecture	Lesson Style (More Details)	Face-to-face, Online (on-demand)
This is a lecture-based class, primarily held in person, with online (on demand) sessions available only in special situations, such as severe weather, the instructor’s business trip, or other exceptional circumstances. The first lesson will be conducted in person.
Credits	2.0	Class Hours/Week	4	Language of Instruction	E : English
Course Level	2 : Undergraduate Low-Intermediate
Course Area（Area）	25 : Science and Technology
Course Area（Discipline）	01 : Mathematics/Statistics
Eligible Students
Keywords	Hypothesis Testing; Sampling and Resampling; Test Statistics and Distributions; Statistical Significance
Special Subject for Teacher Education		Special Subject
Class Status within Educational Program (Applicable only to targeted subjects for undergraduate students)
Criterion referenced Evaluation (Applicable only to targeted subjects for undergraduate students)	Computer Science Program （Abilities and Skills）・A. Information infrastructure development technology, information processing technology, technology that analyzes data and creates new added value. ・B. Ability to identify new problems independently and solve them through quantitative and logical thinking based on data, multifaceted perspectives, and advanced information processing and analysis. Data Science Program （Knowledge and Understanding）・D1. Knowledge and ability to understand the theoretical framework of statistics and data analysis and to analyze qualitative/quantitative information of big data accurately and efficiently. （Abilities and Skills）・A. Information infrastructure development technology, information processing technology, technology that analyzes data and creates new added value. ・B. Ability to identify new problems independently and solve them through quantitative and logical thinking based on data, multifaceted perspectives, and advanced information processing and analysis. Intelligence Science Program （Knowledge and Understanding）・D1. A deep systematic understanding of the advanced intelligence of human beings and its realization by computers. （Abilities and Skills）・A. Information infrastructure development technology, information processing technology, technology that analyzes data and creates new added value. ・B. Ability to identify new problems independently and solve them through quantitative and logical thinking based on data, multifaceted perspectives, and advanced information processing and analysis.
Class Objectives /Class Outline	Understand and apply statistical hypothesis testing, and develop foundational statistical reasoning skills for data science.
Class Schedule	lesson1 Data, Location, and Variability as the Basis of Statistical o COURSE INTRODUCTION o (Chapter 1: Exploratory Data Analysis) o Exploring Data Analysis o Rectangular Data o Estimates of Location o Variability Metrics and Variability Estimates lesson2 Distributional Shape and Association for Exploratory Testing o Exploring the Data Distribution o Percentiles and Boxplots o Frequency Tables and Histograms o Density Plot o Exploring Binary and Categorical Data o Mode, Probability & Expected Value o Correlation o Scatterplots lesson3 Multivariate Structure and Visualization for Statistical Comparison o Exploring Two or More Variables o Hexagonal Binning & Contour Plot o Two Categorical Variables o Categorical and Numeric Data o Visualizing Multiple Variables lesson4 Random Sampling and Sampling Distributions o (Chapter 2: Data and Sampling Distributions) o Random Sampling and Sample Bias o Sample Mean vs. Population Mean o Regression to the Mean o Sampling Distribution lesson5 Central Limit Theorem and Standard Error o Central Limit Theorem o Standard Error lesson6 Resampling, Bootstrap, and Distributional Assumptions o Bootstrap o Resampling vs. Bootstrapping o Confidence Interval Concept o Normal Distribution o Standard Normal and QQ-Plots o Long-Tailed Distributions o Student's t-Distribution lesson7 Confidence Intervals and Discrete Distributions in Testing o Confidence Interval Formula o Binomial Distribution o Chi-Square Distribution o F-Distribution o Poisson Distributions lesson8 Continuous Distributions for Statistical Modeling o Exponential Distribution o Weibull Distribution lesson9 Hypothesis Testing Logic and Experimental Design o (Chapter 3: Statistical Experiments and Significance Testing) o Design of Experiments and Statistical Inference o A/B Testing o Hypothesis Tests o Misinterpreting Randomness o Null Hypothesis o Alternative Hypothesis o One-Sided Versus Two-Sided Hypothesis Tests lesson10 Randomization and Permutation-Based Tests o Resampling o Permutation Test o Exhaustive and Bootstrap Permutation Tests lesson11 p-Values, Error Types, and Statistical Significance o Statistical Significance and p-Values o p-Value from Permutation Test o Alpha in Statistical Testing o ASA (American Statistical Association) Principles on p-Value o Type I and Type II Errors lesson12 Test Statistics and Variance Decomposition o t-Tests o Multiple Testing o Degrees of Freedom o ANOVA o F-Statistic o Decomposition of Variance o Two-Way ANOVA Concept lesson13 Categorical Data Tests and Exact Inference o Pearson Residuals o Chi-Square Statistic o Chi-Square Test: Resampling Algorithm and Statistical Theory o Fisher's Exact Test lesson14 Power, Sample Size, and Sequential Decision Concepts o Multi-Arm Bandit Algorithm Concepts o Power and Sample Size o Power Calculation lesson15 Course Review and Quiz
Text/Reference Books,etc.	P. Bruce, A. Bruce, and P. Gedeck. Practical Statistics for Data Scientists (Second Edition), O’Reilly Media, 2020. [Chapters 1–3 only]
PC or AV used in Class,etc.	Text, Handouts, moodle
(More Details)
Learning techniques to be incorporated	Quizzes/ Quiz format, Post-class Report
Suggestions on Preparation and Review	1. Each lesson's materials (e.g., slides provided by the instructor) should be reviewed both before and after class. 2. The meaning of all English technical terms should be understood before class.
Requirements	Prior coursework in inferential statistics is desirable, along with a working background in either R or Python programming.
Grading Method	The grade will be based on the following two components: 1. in‑class tests, and 2. a final report.
Practical Experience
Summary of Practical Experience and Class Contents based on it
Message
Other	Please bring your own PC, as we will be doing exercises.
Please fill in the class improvement questionnaire which is carried out on all classes. Instructors will reflect on your feedback and utilize the information for improving their teaching.

Back to syllabus main page