Practical notes on popular statistical tests in renal physiology

Competent statistical analysis is essential to maintain rigor and reproducibility in physiological research. Unfortunately, the benefits offered by statistics are often negated by misuse or inadequate reporting of statistical methods. To address the need for improved quality of statistical analysis in papers, the American Physiological Society released guidelines for reporting statistics in journals published by the society. The guidelines reinforce high standards for the presentation of statistical data in physiology but focus on the conceptual challenges and, thus, may be of limited use to an unprepared reader. Experimental scientists working in the renal field may benefit from putting the existing guidelines in a practical context. This paper discusses the application of widespread hypothesis tests in a confirmatory study. We simulated pharmacological experiments assessing intracellular calcium in cultured renal cells and kidney function at the systemic level to review best practices for data analysis, graphical presentation, and reporting. Such experiments are ubiquitously used in renal physiology and could be easily translated to other practical applications to fit the reader's specific needs. We provide step-by-step guidelines for using the most common types of t tests and ANOVA and discuss typical mistakes associated with them. We also briefly consider normality tests, exclusion criteria, and identification of technical and experimental replicates. This review is supposed to help the reader analyze, illustrate, and report the findings correctly and will hopefully serve as a gauge for a level of design complexity when it might be time to consult a biostatistician.