Unraveling the Threads of Statistical Inference: Navigating the Landscape of Uncertainty

Reena Singh

Reena Singh Student, Department of Mathematics, Narottam Singh Padam Singh Rajkiya Mahavidyalaya, Mirzapur.

Abstract

Statistical inference, as the bedrock of data analysis, facilitates informed decision-making in the face of uncertainty. This review article delves into the multifaceted realm of statistical inference, meticulously unraveling its intricacies and exploring the methodologies underpinning hypothesis testing, confidence intervals, and Bayesian inference. Within this landscape, we navigate through the interplay of precision and confidence, demystifying statistical concepts, and examining their practical applications. From the classic frequentist approach to the burgeoning Bayesian paradigm, we delve into the evolving methodologies that shape the understanding and interpretation of data.
The review critically examines the challenges inherent in statistical inference, emphasizing the importance of robust practices to mitigate issues related to sample size, selection bias, and the nuanced interpretation of p-values. It delves into the broader context of the replication crisis, emphasizing the necessity of transparent and reproducible research practices.

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