Statistical Tests in TypeScript Without a Separate Python Service

The article explores whether statistical tests can be implemented directly within TypeScript applications without relying on a separate Python service. It introduces Columna, a library that provides advanced statistical procedures within a JavaScript ecosystem. Columna allows users to perform complex statistical analyses, such as Welch's two-sample t-test, directly within their TypeScript codebase.

The library returns detailed results, including test statistics, degrees of freedom, confidence intervals, and more, which can be easily integrated into the application's UI. The article argues that keeping statistical analysis within the application can simplify the architecture, as no HTTP requests are required for data processing, and the code that decides which observations are included in the analysis can be colocated with the statistical procedure.

This tight coupling allows for easier code review and debugging. However, the author emphasizes that statistical correctness still requires careful consideration of factors such as sample independence, appropriate test selection, and proper handling of missing values. The article cautions against over-reliance on a simple p-value and advises developers to show additional relevant information like sample sizes, descriptive statistics, and confidence intervals.

While Columna provides a starting point for statistical operations in TypeScript, the author suggests that the library should not eliminate the need for verification against established references. The approach is particularly useful for small internal tools, QA dashboards, and educational applications where heavy data processing is handled elsewhere.

The author invites readers to share their experiences with integrating statistical operations into TypeScript applications and where they draw the line between keeping small features local and offloading them to larger data platforms.