This paper analyses the stability and distribution of ambiguity attitudes using a broad population sample. We employ four waves of data from a survey instrument with high-powered incentives. Structural estimation of random utility models yields three individual-level parameters: Ambiguity aversion, likelihood insensitivity or perceived level of ambiguity, and the variance of decision errors. We demonstrate that these parameters are very heterogeneous but stable over time and across domains. These contexts span financial markets—-our main application—-and climate change. The ambiguity parameters are interdependent in their interpretation and the precision of their estimates depends on decision errors. To describe heterogeneity in these three dimensions, we adopt a discrete classification approach. Thirty percent of our sample come rather close to the behaviour of expected utility maximisers. Half of the sample is characterized by a high likelihood insensitivity with about equal shares being ambiguity-averse and ambiguity-seeking on average, respectively. For the remaining twenty per cent, we estimate sizeable error parameters, which implies that no robust conclusions about their ambiguity attitudes are possible. Predicting group membership with a large number of observed characteristics shows reasonable patterns. Even after controlling for a many covariates, ambiguity types predict risky asset holdings in the expected direction.