Ioannidis on COVID-19

"A Conversation with John Ioannidis"

You can't trust the weather man

1. Thanks to Steve Hays for sending me this article by Dr. John Ioannidis. Ioannidis is a Greek physician and epidemiologist. He's most famous for (rightly) taking to task academic scientific publications and showing how the vast majority of them are basically bad science and/or bad statistics. Ioannidis' article on the coronavirus is worth reading. As an ID physician said about the same article: "Excellent piece by ⁦@METRICStanford on CFRs, the calculus of social distancing, and how lack of data is compromising decisions."

2. Similarly I earlier pointed out that Nate Silver (whose day job is predicting political elections on his 538 website) brought up some limitations in coronavirus models too.

3. I think epidemiological models might parallel meteorological models. That is, I think epidemiological models attempting to predict how bad the coronavirus will be might be similar to weather reports trying to predict the weather. We can't accurately predict the weather 100% of the time. Let alone in every place in the world. Sure, there may be general accuracy, but weather reports notoriously get things wrong too. Just my general impression, but I'm no epidemiologist or meteorologist or statistician or the like.

Of course, this doesn't necessarily mean we shouldn't bring an umbrella if the weather report predicts it's going to rain, but it also doesn't necessarily mean we should. It depends on a number of factors. Such as the further out the weather forecast is, the more unreliable it is likely to be. It's normally more accurate to predict it will rain this evening than it is to predict it will rain tomorrow or one week from now or one month from now.

4. I guess the only sure thing about predictions regarding the weather, political elections, the future impact of disease on populations, what the stock market will do, and who will win the Super Bowl is that there's bound to be someone in the end who will say: "See, I told you so!" :) And sometimes they might even be right. Sometimes.

Why science is not necessarily self-correcting

Abstract:

The ability to self-correct is considered a hallmark of science. However, self-correction does not always happen to scientific evidence by default. The trajectory of scientific credibility can fluctuate over time, both for defined scientific fields and for science at-large. History suggests that major catastrophes in scientific credibility are unfortunately possible and the argument that “it is obvious that progress is made” is weak. Careful evaluation of the current status of credibility of various scientific fields is important in order to understand any credibility deficits and how one could obtain and establish more trustworthy results. Efficient and unbiased replication mechanisms are essential for maintaining high levels of scientific credibility. Depending on the types of results obtained in the discovery and replication phases, there are different paradigms of research: optimal, self-correcting, false nonreplication, and perpetuated fallacy. In the absence of replication efforts, one is left with unconfirmed (genuine) discoveries and unchallenged fallacies. In several fields of investigation, including many areas of psychological science, perpetuated and unchallenged fallacies may comprise the majority of the circulating evidence. I catalogue a number of impediments to self-correction that have been empirically studied in psychological science. Finally, I discuss some proposed solutions to promote sound replication practices enhancing the credibility of scientific results as well as some potential disadvantages of each of them. Any deviation from the principle that seeking the truth has priority over any other goals may be seriously damaging to the self-correcting functions of science.

Source: "Why Science Is Not Necessarily Self-Correcting" (pdf) by John P. A. Ioannidis.

Is science self-correcting?

"Why Science Is Not Necessarily Self-Correcting" (pdf) by John P. A. Ioannidis.

(Ioannidis is perhaps best known for his paper "Why Most Published Research Findings Are False".)