The shape of it is that there is a statistic about population and then that statistic is used to describe a member of that population. For example, a news story that starts with “70% of restaurants fail in their first year, so it’s surprising that new restaurant Pete’s Pizza is opening their third location!”
But it’s only surprising if you know absolutely nothing about Pete and his business. Pete’s a smart guy. He’s running without debt and has community and government ties. His aunt ran a pizza business and gave him her recipes.
In a Bayesian way of thinking, the newscasters statement only makes sense if the only prior they have is the average success rate of restaurants. But that is an admittance that they know nothing about the actual specifics of the current situation, or the person they are talking about. Additionally there is zero causal relationship between group statistics and individual outcomes, the causal relationship goes the other way. Pete’s success will slightly change that 70% metric, but the 70% metric never bound Pete to be “likely to fail”.
Other places I see the “bound by statistics” problem is in healthcare, criminal proceedings, racist rhetoric, and identity politics.