People react to statistics very differently than they react to concrete examples, even though statistical generalities mean that there exist many concrete examples. Of course there are systematic differences between generalities and individual examples. For example, a concrete example might not be representative. Indeed, it probably is not representative for the very reason that it is at hand. But there are many other ways that people react differently that seem to me worthy of study.
I will compare two stories of political corruption, one statistical and one concrete that seem to me to have had rather different responses. It wouldn’t terribly surprise me if people had failed to believe one or the other. (Which would you expect?) But both of these stories were largely accepted as explained by corruption. Yet within the domain of corruption, the explanations of exactly how it was done were very different, practically disjoint.
The statistical evidence is the study by Ziobrowski, Cheng, Boyd, and Ziobrowski of the stock portfolios of US Senators. They found that stocks held by Senators outperformed the market, with both purchase and sale significant events. People generally accept this as corruption. People usually attribute the result to “insider trading,” and debate several specific theories: that Senators purchase based on their knowledge of upcoming legislation, or corporate information that leaks out in hearings; more corruptly, that stock ownership influences legislation, or that corporate insiders bribe Senators with corporate information.
The concrete example is that Hillary Clinton made a lot of money on cattle futures. This, too, is generally accepted as corruption. But I have never heard anyone put forward any of the four theories above to explain what happened there. Nor have I seen the specific explanation of the cattle futures trades put forward as an explanation of the Senate data. The popular explanation is that Clinton never made any bets based on any information, but that the trades were falsified after the fact to provide a paper trail to launder a bribe.
Of course, it is possible to reconcile the reactions. Perhaps people have a much higher prior on the first four hypotheses than on the fifth, so that they only consider it when the first four have been ruled out; though they don't discuss the process of ruling them out. Or, perhaps, it reflects important differences between stocks and commodity futures. But I think it is more likely that the reactions are inconsistent, one of them worse than the other. I think it likely that the different reactions reflect the concrete versus statistical natures of the two claims and other aspects of the context. Insider information is a standard answer provided by the context of an economics journal. More generally, statistical summaries sound definitive, so they demand to be explained, not to be rejected. While people are more willing to call bullshit on a story, even though a statistic is just a bunch of stories. Even in the context of already accusing large numbers of people of corruption. But I don’t know what drives the difference. I propose it as a question, not an answer.