Abstract
Statistical analysis has been at the heart of scientific research, providing critical tools for data interpretation, decision-making, and hypothesis testing. Some of the ancient techniques used were hypothesis testing, regression analysis, and time series analysis, among others. Such methods have proved to be good tools for researchers dealing with smaller and more structured datasets. However, the large size and complexity of the dataset exposed the weaknesses in such classical approaches, especially in handling large, unstructured, or non-linear datasets. Increased computing power and the development of machine learning algorithms have increased flexibility, nudging the statistical approach towards more flexible, data-driven methods. This paper reviews emerging approaches such as machine learning, deep learning, Bayesian methods, and network analysis and places an emphasis on how these approaches can be applied over a range of fields where they could vastly transform statistical analysis. Based on this review, comparing traditional and modern methodologies, it thereby demonstrates how innovations better complement rather than replacing the capabilities of statistical analysis, thus shaping the future of research in this changing environment.
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