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This paper focuses on the role of statistics in big data analytics to build analytical accuracies and precise decision-making insights. Statistics and data mining techniques that are useful for big data analytics include: significance testing, classification, regression/prediction, cluster analysis, association rule learning, hypothesis testing, anomaly detection, and visualization. Statistical analysis provides a scientific justification to move from data to knowledge and turn it into actionable insights, emphasizing their role in data preprocessing, pattern recognition, and predictive modeling. The study navigates through prominent statistical methods such as regression analysis, hypothesis testing, and machine learning algorithms, illustrating their efficacy in extracting actionable knowledge from vast and complex datasets. Moreover, it sheds light on the symbiotic relationship between statistics and technological advancements, showcasing their collaborative potential in driving innovation within the Big Data domain. By synthesizing current research and practical applications, this paper aims to underscore the indispensable nature of statistical methodologies in harnessing the power of Big Data for informed decision-making. In the era of Big Data, where information proliferates at an unprecedented scale, the role of statistics has become increasingly vital in extracting meaningful insights from the vast and complex datasets that characterize this landscape. This paper seeks to unravel the intricate interplay between statistics and Big Data analytics, elucidating the significance of statistical methodologies in navigating the challenges posed by the sheer volume, velocity, and variety of data
Suthar et al. (Sat,) studied this question.