In Praise of Observational Evidence
7th July 2026
These early tests of quantitative hypotheses illustrate both the risks and merits of observational evidence. To be sure, as Arbuthnot showed, it’s easy enough to find that the data proves something you wanted to be true all along. Yet it is remarkable that both Arbuthnot and Laplace could obtain local records and start doing science right from their desks. In doing so, both correctly documented important phenomena without the need to invest much labor or capital to get the data.
The efficiency, simplicity, and beauty of this method of gaining knowledge has been underappreciated, especially in medicine and public health. Although it is considered ideal to obtain data in the form of a randomized control trial for an intervention like a drug, that form of data collection is not always possible in either field. Granted, the Paris versus London hypothesis Laplace was testing is relatively simple, but his sample size was over 1.93 million, more than the vast majority of interventional trials in the history of medicine. It is a rare randomized trial (studying a particular intervention with a sufficiently large and well-distributed sample population) that can detect an 0.3% difference in a binary random variable — but Laplace could, more than 200 years ago.
The advantages of the RCT have cemented it as the gold standard for interventional trials in medicine, and it remains what many laypeople think of as the one true way to do science. Yet once we understand where these advantages come from, how they interact with the economics of collecting samples, and the merits of the alternative, observational evidence emerges as the winner more often than one might think.