In the 40 years since “Some Statistical Methods Useful in Circulation Research” was published, many of the same battery of statistical tests and concepts, such as t-tests, ANOVA, p-values, effect sizes, and standard errors, are still abundantly employed in hypothesis-driven research. Newer methods, too, have emerged to address the challenges of big data analysis. Some methods now routinely employed to extract insights from data include regression analysis, supervised and unsupervised machine learning for clustering, density estimation, and dimensionality reduction (e.g., viSNE), as well as prediction modeling and enrichment analyses. Additionally, in basic science research, it is now common to encounter hypothesis-free analyses, in marked contrast to traditional statistical analyses that begin with an explicit hypothesis. To encourage reproducibility, rigor, interpretability, and transparency, many editorial teams, including those of Circulation Research and the AHA journals, have developed statistical guidelines for authors. Given the rapidly changing data landscape, such guidelines must extend beyond “What statistical test should I use?” (a question that often can be addressed by a decision tree diagram in applied statistical analysis textbooks), to address higher-level challenges that frequently face authors including multiple testing, standards of reporting, robustness to violations of assumptions, and the limitations of conventional measures of significance. To better support authors and readers, we have assembled some topics that warrant particular attention in basic and clinical scientific publications such as those published in Circulation Research. These guidelines are intended to complement those outlined by the American Heart Association’s Statistical Taskforce in their concurrent “Guidelines for Statistical Reporting in Cardiovascular Medicine: A Special Report from the American Heart Association”
