Misleading Conclusions: Noisy data can lead to incorrect inferences, affecting public health policies and interventions. Increased Costs: Analyzing noisy data often requires additional resources for cleaning and validation. Reduced Confidence: Stakeholders may lose trust in the findings derived from noisy datasets.
