COVID-19 and Consensus Confusion

When people talk about consensus, it generally means that there is an overwhelming agreement. The public has a strange mixture of admiration and suspicion for scientists, but they tend to fall in line when confronted with "consensus science" claims about universal common descent evolution, anthropogenic climate change, and more. With the COVID-19 crisis, we are learning more about how scientists act.

The  COVID-19 crisis has taught scientists many things, and taught many people about science and worldviews.
Made from Piltdown Gang by John Cooke, 1915
As we saw in "An Abundance of Dubious Models", healthcare professionals and scientists had to learn as much as possible in a short amount of time. Not only does the COVID-19 coronavirus research have leftist political influences, but there is no "consensus science". Indeed, the professor with the dire predictions that affected so many people was so wrong, he resigned in disgrace. We also see quite clearly that science is no done from an intellectual or morally neutral approach: everyone interprets data from their own worldviews, so the overall picture is not so sharp.
Are people growing weary of scientific experts giving them contradictory instructions for dealing with the COVID-19 pandemic? It seems that there may be an increasing willingness by politicians and ordinary citizens alike to question the reliability of scientific pronouncements by experts—at least by those addressing this pandemic. Some state governors have taken to interpreting the data (as well as all of the different expert opinions) for themselves. This has led to different state responses. . . .
Getting scientific agreement on relatively straightforward interpretations of data is simple enough. If a panel of experts was asked the question: Is the spread of COVID-19 an outbreak, an epidemic, or a pandemic? The data clearly support the interpretation that the world is facing a pandemic. However, agreement on interpretations of data breaks down quickly when more complex questions are applied to it. One of the best studies illustrating this problem was recently published as it relates to neuroimages.
To read the full article, click on "Different Interpretations of Same Data Is Routine".