Statistics BasicsGetting Ready
Here, we explain the fundamentals of statistics and practical statistical methodsfrom the perspective of statistical professionalsin an accessible manner.
Some sections are currently under development and may not yet be available. We will update them over time.
About Data
Data can broadly be divided into two types: numerical data and qualitative (categorical) data. Numerical data represent quantitative values, such as height, weight, age, income, and sales. Numerical data can be further divided into continuous and discrete values. Height and weight are continuous, while values such as age and income may be treated as discrete. Categorical data represent categories or attributes, such as sex, occupation, prefecture, or product type. Categorical data include nominal and ordinal scales. A nominal scale distinguishes categories without an inherent order—for example, prefectures or product types. An ordinal scale has a meaningful order among categories—for example, survey responses such as “often,” “sometimes,” and “rarely.” This section introduces the basics of data.
View DetailsInterrelationships Among Variables
Multivariate analysis is a general term for statistical methods that analyze interrelationships among multiple variables. Common examples include correlation analysis, principal component analysis, factor analysis, cluster analysis, regression analysis, and multiple regression analysis.
Compared with univariate analysis, which analyzes a single variable, multivariate analysis can handle more complex data and is often better suited to real-world problems. It is useful, for example, when analyzing statistical datasets with many variables or market-research data containing multiple questionnaire items.
Multivariate analysis often requires data preprocessing and visualization. Because interpretation also requires a foundation in statistics, experienced analysts are preferable when accurate and meaningful results are needed.
This section introduces analytical methods for numerical data.
Types of Statistical Tests
A statistical test is a method used in data analysis to evaluate a hypothesis or assumption. Specifically, it examines whether a result obtained from data differs from what would be expected under a particular hypothesis and uses that evidence to decide whether to reject or retain the hypothesis. For example, in market research, if survey responses about a product are collected, one can test whether the observed mean differs from the mean expected in a particular population. The sample mean is compared with the population mean to assess whether the observed difference could reasonably be explained by chance. If the difference is unlikely to be due to chance under the assumed model, the result provides evidence against the null hypothesis. Common statistical tests include t-tests, z-tests, F-tests, and chi-square tests. The appropriate method depends on the type of data and the research question.
This section introduces practical methods for statistical testing.
Analysis of Text Data
Qualitative data analysis primarily deals with non-numerical information and includes methods for analyzing data obtained from text, images, audio, and video. It is widely used in social sciences and humanities, as well as healthcare, education, and other fields concerned with human behavior, thinking, and emotions. Methods include text mining, content analysis, thematic analysis, framework analysis, and qualitative interviews. These approaches classify qualitative data appropriately, examine similarities and differences, and generate new insights and understanding. This section introduces practical examples of qualitative data analysis.
View DetailsGrounded Theory Approach
M-GTA is the Modified Grounded Theory Approach developed by Professor Yasuhito Kinoshita. It substantially reorganizes the original GTA and establishes a distinctive research methodology with a systematic approach to concrete data analysis. It uses an analytical worksheet consisting of four elements: concept name, definition, variations, and theoretical memo. Theoretical memos record alternative interpretations, negative cases, similar cases, and related observations beyond the formal definition. By reviewing relationships among concepts and categories and the final overall structure, researchers can reduce the risk of pushing interpretation in only one direction. This section introduces practical examples of M-GTA.
View DetailsThematic Analysis
Thematic analysis is a widely used method in qualitative research for identifying themes and categories in text data such as documents, interviews, and focus groups in order to develop a deeper understanding of meaning. The process typically begins with careful reading of the entire dataset to identify recurring themes and patterns. These are then organized to categorize concepts, ideas, emotions, behaviors, and other relevant features in the data. The resulting categories are analyzed to develop new insights from the qualitative material. This section explains practical procedures for thematic analysis.
View DetailsExamples of Medical Statistics
Statistical analysis is now used frequently in medicine and healthcare. Applications include disease statistics, cancer registries, hospital management, clinical research, epidemiological surveys, and healthcare-service-area analysis. These analyses support diagnosis and treatment, management decisions, and materials for healthcare administration. It is therefore important not only to be able to perform basic statistical analyses but also to understand their theoretical background. This section presents practical examples focused on skills needed to use clinical information effectively, interpretation of analytical results, and understanding of statistical theory.
View DetailsDifferences Among Statistical Software Packages
Statistical software provides useful tools for data analysis and statistical processing. Common packages include R, Python, SPSS, Stata, and Excel. R, for example, is open-source statistical software used for a broad range of tasks including data visualization, statistical analysis, and machine learning. Because R is also a programming language, it is highly flexible and allows users to create custom functions. This section introduces different types of statistical software and their characteristics.
View DetailsRecommended Books on Statistics
To learn statistics efficiently, it is important to understand your current level. Even when a book appears to cover material that seems obvious, knowing something and truly understanding it are different. Rather than looking for shortcuts, choose books appropriate to your level. As with any field, compatibility between the reader and the book matters. In academic study, materials that do not match the learner's ability or purpose can be difficult to understand.
This section introduces accessible introductory books as well as reference books and textbooks for people who want to study statistics.
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