The emergence of a term for massive data sets called Big Data that may be analysed computationally, with high volume, velocity, and variety, has therefore made it imperative to reorient analytical paradigms. This paper discusses the essential role of statistical methods in navigating large-scale datasets and in transitioning from classic hypothesis-based studies to modern data-driven exploration. It studies how statistical methods are developed and improved to deal with significant issues like noise, scalability, and lack of integration. This is followed by a systematic literature review and a case study of two benchmark sectors: e-commerce and healthcare, where specific measurement methods are discussed. Our result underscores the crucial complementarities between basic statistical principles and complex machine learning algorithms in predictive analytics and pattern recognition. Nonetheless, despite this somewhat critical view on algorithmic fairness, the paper's overall finding was that there appears to be a significant gap in research progress, which is of ethical importance as a technical advancement in Big Data analytics. We argue here that successful Big Data analysis thus demands a dual-pronged approach, one based on computational innovations and the other on robust data governance to ensure subsequent insights are not only correct statistically but also fair and reliable, closing in a loop with all layers of society.