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Okay.

So what's a second reason for this decline effect for the truth wearing off?

Well, another one is just people make mistakes and, in some cases,

there's instances of fraud.

And so there's some evidence here that these kinds of things are going up as

well, as measured.

One way to measure this is by the number of paper retractions.

And the number of retractions has gone up fairly significantly as Neorden

pointed out in 2011 in an article in Nature.

So from the period of time 2001 to 2011 there's been a 10 fold increase

in number of paper retractions but only a 1.44 fold increase in themselves.

Okay. And so

this plot is from two different depositories.

PubMed and Web of Science.

Neither one of which can include computer science papers, by the way.

But you can see that it's gone up pretty significantly.

So we're not gonna dive into too much detail into this phenomenon.

But I do want to give you one statistical Tool that is

sometimes used to detect fraud.

And that's Benford's Law.

So if you're not familiar with Benford's Law, it's a fun one to be familiar with.

So this law predicts the distribution

of the first digit of data.

And if you think about it without thinking too deeply,

you might intuitively think that this would be fairly uniform.

If you have random data, you might see an equal number of eights.

And an equal number of threes, an equal number of twos, and so on.

But it turns out, that the distribution is not random.

In some circumstances, in many circumstances.

It's not even, I should say, it is random.

It’s just not even, the distribution looks more like this.

You will get more ones in the first position than you will twos, and more twos

than threes and so on, until up to 30% of the numbers you measure are ones.

So this should, blow your mind a little bit, okay.

So some examples of this before we explain what's going on, taken from.

There’s a nice website testingbenfordslaw.com where they

pull real data in and show the plots.

This is the number of Twitter users by the numbers of followers they have.

Sorry not the number of Twitter users.

Twitter users by the number of followers they have.

Okay?

Alright, so the list of numbers is just the number of followers.

The number of Twitter users whose number of followers begins with

the digit one Is 32.62% if you can see that.

And the red dot is the prediction made by Benford's Law.

So, not too bad.

The number of instances that have the two as leading digit is 16.66% and

there's the red dot predicted by the original and so on.

So, not too, not too bad.

The distance of stars from Earth in light years follows a similar pattern.

This is remarkably close to Benford's law prediction.