Differential privacy distinguishes between learning about individuals and populations by ensuring that analysis results are not dependent on any single individual's data. It allows for the study of population-level trends without revealing specific information about individuals. For example, if analysing a dataset leads to a change in beliefs about a population characteristic, it is not considered a privacy breach if the same conclusion would have been reached regardless of any particular individual's data. This approach separates the impact of learning about the population as a whole from the privacy of individuals in the dataset.