Epistemic Risks of Big Data Analytics in Scientific Discovery: Analysis of the Reliability and Biases of Inductive Reasoning in Large-Scale Datasets

The advent of Big Data Analytics has transformed scientific research by enabling pattern recognition, hypothesis generation, and predictive analysis across disciplines. However, reliance on large datasets introduces epistemic risks, including data biases, algorithmic opacity, and challenges in inductive reasoning. This paper explores these risks, focusing on the interplay between data- and theory-driven methods, biases in inference, and methodological challenges in Big Data epistemology. Key concerns include data representativeness, spurious correlations, overfitting, and model interpretability. Case studies in biomedical research, climate science, social sciences, and AI-assisted discovery highlight these vulnerabilities. To mitigate these issues, this paper advocates for Bayesian reasoning, transparency initiatives, fairness-aware algorithms, and interdisciplinary collaboration. Additionally, policy recommendations such as stronger regulatory oversight and open science initiatives are proposed to ensure epistemic integrity in Big Data research, contributing to discussions in philosophy of science, data ethics, and statistical inference.