Eric Topol

But during the pandemic, we had things like the six-foot rule that was never supported by data, but yet still today.

Like if I walk around my hospital and there's still...

the footprints of the six foot rule and, you know, not paying attention to the fact that this was airborne and took, you know, years before some of these things were accepted, the flatten the curve stuff with lockdowns, which, you know, I never was supportive of that, but the,

You know, perhaps at the worst point, the idea that hospitals would get overrun was an issue.

But, you know, it got carried away with school shutdowns for prolonged periods.

And, you know, in some parts of the world, especially, you know, very stringent lockdowns.

But anyway, we learned a lot.

But perhaps one of the greatest lessons is that people's expectations about science are

Is that it's absolute and somehow you have this truth that's not there.

I mean, it's getting revised.

It's kind of on the job training.

It's, in this case, on the pandemic revision, but very interesting.

And that gets us to, I think, the next topic, which I think is a fundamental part of the book distributed throughout the book, which is the different types of proof.

in biomedicine, and of course, across all these domains.

And so you take us through things like randomized trials, p-values, 95% confidence intervals, counterfactuals, causation and correlation, peer review, the works, which is great, because a lot of people have misconceptions of these things.

So for example, randomized trials, which is like the temple of the randomized trials,

They're not as great as a lot of people think.

Yes, they can help us establish cause and effect, but they're skewed because of the people who come into the trial.

So they may not at all be a representative sample.

What are your thoughts about over deference to randomized trials?