But how are they different, these things?

So they can be different in many ways, including the evidence to support them.

the way in which they were developed, which also affects their probative value, meaning what are they really there? What can they really show? And then the constructs that seem to be assessed.

In terms of the procedures, we set out to do it right.

And so that's when I contacted Emily and I said, you know what? Let's do this right.

This is timely.

This is needed for the field.

And just from the basics, like people are like, how is this different than hunger or cravings? So like, and I explain it, but I'd love to hear you all explain the difference between those different concepts.

Of those projects, what do you think, you know, of those that you mentioned could, could have the most meaningful impact in the next few years, and why do you think they would have that impact?

I, I think that there's impact at the scientific level, and then I think there's impact at the, uh, applied or clinical level, you might say.

Both are hard to predict.

At the scientific level, I think that, uh, the work I just mentioned on oxalate, I think that some of the work...

Uh, and that's by the way, Paul Fukata's laboratory.

Uh, some of the work being done by Dr. Yi Zhu, uh, Z-H Zhu, in our group on the molecular, uh, effectors of outcomes with respect to cardiometabolic health, uh, being done in mice.

He's on the trail of some very interesting genetic influences, um, and that those, some of those influences which have transgenerational effects.

In, in, in your history and with all your experience, what are the most common ways that mismatch actually happens?

Why is this such a problem? And then why do you think this remains to be so common?

I think it's a problem for two reasons.

One is, you might say, is innocent, and the other is not so innocent.

The innocent problem is that many non-statistician investigators don't understand statistics very well.

The most common type of statistical inference we use, and inference meaning, you know, you're sort of making a-- not just describing what you see in the data, in the sample data you have in your hand, but you're making an inference to what's the case in the population from which you've obtained that sample.

That's what you really want to know about.

And so the process of inference, one school is called the frequentist school and is by far the most common o- one used in the health sciences, and it involves the P values and the confidence intervals and things like that that you're used to seeing.

So with that ramble aside, can you maybe mention for maybe people who are not in the f- field, why is it so problematic if someone is just going to measure a whole bunch of different outcomes that they're not our primary outcome, and then after the fact, just see what ends up being a significant outcome?And I suppose the real thrust of the question is, if we do find significance within that particular secondary outcome, let's say, why does that not necessarily guarantee that's a trustworthy piece of data, let's say?

Is that basically the argument?

You are absolutely correct, and when you said statistics in the... or power rather, in the beta sense, that is what it is.

It is statistical power, the probability of rejecting the null hypothesis if the null hypothesis is false, which is one minus beta, the type two error rate.

There's actually a little bit more but I think 90, 90-plus% of the motivation is what you've described.

The other, as actually Kevin pointed out to me in some dialogue we were having, was sometimes it's just throughput.

Even if I had infinite money, I can't put people through the procedure fast enough because there's only so many chambers.

As someone else pointed out to me, it could be patient availability.

So, by that logic, why are we having this discussion? Why don't we just get rid of ultra-processed foods?