Six Maxims of Statistical Acumen for Astronomical Data Analysis

TL;DR

This paper presents six guiding principles for improving statistical and machine learning analysis in astronomy, emphasizing understanding limitations and nuances to enhance data interpretation and scientific accuracy.

Contribution

It introduces six maxims of statistical acumen tailored for astronomical data analysis, aiming to elevate analytical skills and scientific rigor in the field.

Findings

Six maxims serve as cautionary guidelines for astronomers.

Applying these principles can improve data analysis quality.

Enhanced understanding of statistical limitations benefits scientific conclusions.

Abstract

The production of complex astronomical data is accelerating, especially with newer telescopes producing ever more large-scale surveys. The increased quantity, complexity, and variety of astronomical data demand a parallel increase in skill and sophistication in developing, deciding, and deploying statistical methods. Understanding limitations and appreciating nuances in statistical and machine learning methods and the reasoning behind them is essential for improving data-analytic proficiency and acumen. Aiming to facilitate such improvement in astronomy, we delineate cautionary tales in statistics via six maxims, with examples drawn from the astronomical literature. Inspired by the significant quality improvement in business and manufacturing processes by the routine adoption of Six Sigma, we hope the routine reflection on these Six Maxims will improve the quality of both data analysis and scientific findings in astronomy.