Version 2.051

When my ex-husband was getting an undergrad in business, one day he got home all excited because his accounting professor had gone over some techniques on how to spot data manipulation. That was the first accounting class he'd ever taken and someone thought it worthy to add that to the curriculum. I, on the other hand, had taken numerous accounting classes in Portugal, both in high school and college, and did not recall ever talking about data manipulation. The shadiest topic we ever covered was "confidential expenses" and the so-called blue bag.

The first thing one does when analyzing data is evaluating how reliable the data are. How was it collected, what biases might it have, what limitations does it have and how do those shape our conclusions. Then, if there is information on more than one variable, does it all make sense together -- do things pass the smell test, as one of my former bosses used to say.

I am thinking about these issues because it was obvious that the pandemic data was going to be heavily scrutinized, so one should better lay the cards out on the table and be as transparent as possible. Consider that as a kind of preemptive strike: if later on, anyone casts doubts on the numbers or our efforts, we could always defend ourselves by saying that we had been very transparent with the data and that we were hoping that such transparency would allow the scientific community at large to help us garner better information more quickly for the sake of saving and protecting lives. That is what anyone with half a brain would do.

"One can fool some men, or fool all men in some places and times, but one cannot fool all men in all places and ages."

~ Jacques Abbadie, 1684