Just this evening some Italian scientist announced on his twitter account that the number of new positive cases does not correspond to the number of new tests carried out and announced for that day, because those tests could have actually been made 2 or 3 days before or something like that.

Which, presumably, instantly invalidated all the charts and data-modelling based on the "number of new positive cases" / "total number of tests made" (with a lower value being seen as better).

But the Region of Sicily (or its official twitter account, anyway) replied that in their case the number of new positive cases and the total number of tests made are indeed correlated, which of course means that everything is a mess in terms of data coming in and its significance.

Later edit: For those who know Italian this is the tweet [1] I was writing about, and it looks like I was remembering wrong, the guy is not a scientist per se, more like a "data scientist", he seems to be working at a company very similar to fivethirtyeight (but presumably focused on the Italian market).

[1] https://twitter.com/lorepregliasco/status/124827958933764505...

GIGO is a principle i learned at audio engineering school and it applies to all input/output relationships across multiple disciplines.

Have a 4k tv but only a 1080 cable box? Your tv will only show 1080.

Did you record too much compression on the master track? Now you have to deal with that in the mix.

GIGO is any time you get an input that will never give a desired or expected output. In circuits, its easy. It works or it doesn’t. In data science, GIGO can be hidden by hiding the assumptions made to reach the level of knowable information from the data provided.

Data scientists have a huge uphill battle right now. It is far more complex than looking at the numbers and trying to find patterns. People can find patterns in clouds.