In vivo studies remain a cornerstone of biomedical, pharmacological, and toxicological studies, providing critical insights into the safety and efficacy of novel interventions. However, the reliability and translational value of such experiments are frequently undermined by methodological errors, hidden contaminations, and uncontrolled confounding factors. Poor study design, insufficient randomization, and operator-related inconsistencies introduce variability that may obscure true biological effects. Similarly, viral or microbial infections, environmental contaminants in feed or bedding, and cross-contamination between animals can profoundly alter immune, metabolic, or behavioral outcomes, often without being detected until results prove inconsistent. Furthermore, factors such as temperature, light cycles, handling stress, circadian rhythms, and biological characteristics of the animals introduce additional layers of complexity, leading to irreproducible or contradictory findings. The present study synthesized current evidence on the most common sources of error, contamination, and confounding in in vivo studies, illustrated with practical case examples. It further highlighted best practices for mitigation, including protocol standardization, adherence to ARRIVE guidelines, the use of specific-pathogen-free animals, continuous monitoring of environmental and health parameters, and comprehensive staff training. Emerging solutions such as automation, artificial intelligence, and the progressive integration of in vitro and in silico alternatives are discussed as pathways toward reducing dependence on animal models. By identifying threats to validity and offering practical solutions, this review aimed to enhance reproducibility, reduce unnecessary expenditure of time and resources, and improve the ethical and scientific integrity of in vivo studies, thereby strengthening their translational value in human studies.