Differential privacy uniquely distinguishes between learning about a population as a whole and learning about individual members within it. This approach allows for the analysis of population-level trends and characteristics without revealing specific information about any individual. For instance, understanding that smoking is linked to health issues is valuable for public health, but this knowledge doesn't need to expose whether a particular individual in the dataset smokes. This distinction is crucial because it allows for meaningful statistical analysis that can inform policy and decision-making while ensuring that individual privacy is not compromised. The key is that conclusions drawn from the data would remain the same regardless of any one individual's participation in the dataset.