The role of statistics in advancing nitric oxide research in plant biology: from data analysis to mechanistic insights.

Nitric oxide (NO), a key signaling molecule in plants, induces various biological and biochemical processes, including growth and development, adaptive responses, and signaling pathways. The intricate nature of NO dynamics requires vigorous statistical approaches to guarantee precise data interpretation and significant biological conclusions. This review underscores the importance of statistical methodologies in NO study, discussing experimental design, data collection, and advanced analytical tools. In addition, vital statistical challenges such as high variability in NO measurements, small sample sizes, and complex interactions with other signaling molecules, are investigated along with approaches to alleviate these limitations. New computational techniques, including machine learning, integrative omics approaches, and network-based systems biology, present commanding outlines for identifying NO-mediated regulatory mechanisms. Furthermore, we underscore the necessity for interdisciplinary collaboration, open science practices, and standardized protocols to improve the reproducibility and dependability of NO research. By combining robust statistical methods with advanced computational tools, researchers can gain enhanced insights into NO biology and its effects on plant adaptation and resilience.