In his interview, behavorial economist David Laibson emphasizes the importance of understanding estimates of uncertainty as both point estimates and distributions of possibilities. In particular, these distributions may have tail events (i.e. low probability events) that happen more likely than expected by the models. My question is if tails of the distribution on events are fatter (i.e. more likely) than expected, then why are models not being corrected so that the tails are more accurate? For example, tail events in the stock market, such as market crashes or bubbles, may be more likely than expected than in a symmetric Normal distribution of events. Or is the notion of a tail event an example of bias or small sample size? Because we observe that some event with very low probability occurred, we may think it should have higher probability, but our sample size may be too small to compare the empirically observed frequentist probability to the theoretical probability given by the model.