Extrapolating data from laboratory settings to real-world scenarios is fraught with uncertainty. Laboratory conditions are controlled and often involve homogenous populations, whereas real-world exposures are variable and involve diverse populations. This discrepancy can introduce significant uncertainty in risk assessments. Furthermore, extrapolating data from short-term studies to predict long-term effects adds another layer of complexity and uncertainty.
