Discover how propensity score analysis (傾向スコア分析) enhances observational studies in Japan. Learn practical applications and methodologies for more robust research outcomes.
Sensitivity Analysis in Observational Studies: Quantifying Unmeasured Confounding
Discover how sensitivity analysis and E-value quantify unmeasured confounding in observational studies, enhancing the robustness of your research findings.
Canonical Correlation Analysis: Exploring Relationships Between Variable Sets in 2024
Albert Einstein once said, “The important thing is not to stop questioning. Curiosity has its own reason for existing.” This idea is very true in data analytics. Here, looking into variable relationships can show us new things in our data. In 2024, Canonical Correlation Analysis (CCA) is a key multivariate statistical technique. It looks at how two sets of variables are connected, showing us things…
Non-Parametric Statistical Methods: When to Use Them in 2024
Albert Einstein once said, “Everything should be made as simple as possible, but no simpler.” This idea is very true in statistical analysis, especially for non-parametric methods. These methods are a strong choice for data analysis when traditional tests don’t work well. In 2024, knowing when to use them is key for accurate statistical analysis and testing hypotheses. Non-parametric statistical methods are great for data…
Statistics for Non-Statisticians: Making Sense of Data
Did you know the BISG family of algorithms can match race and ethnicity with 92–98% accuracy1? For many, statistics seem like a mystery, full of complex formulas and hard words. But, statistics are key to understanding the data that affects our lives and businesses. Statistics for Non-Statisticians: Making Sense of Data (2024 Edition) [Brief Note] Statistics for Non-Statisticians: Making Sense of Data (2024 Edition)…