zlacker

>>bookof+(OP)
Fake is generally the wrong word. Inaccurate would be much more appropriate. Every population estimate is just that. There is going to be error. The error may be small or large, and it may be biased in one direction or another, but there is a clear chain from data to result. Even if your data sources are fraudulent, if you're making any attempt to account for that, though you may not do a very good job, it's still just inaccuracy. Fake would imply that the people releasing the population estimates have a much better estimate but are choosing to instead publish a made up number. This may actually happen in a few cases, but the claim that it's widespread is both hard to believe and unsupported by this article.

>>jjk166+Iq
> Fake would imply that the people releasing the population estimates have a much better estimate but are choosing to instead publish a made up number.

That is literally what the article describes, though, in Papua New Guinea. And it describes why states in Nigeria have such a strong incentive to fake their population numbers, that it's impossible to achieve an accurate national total.

I do think the headline exaggerates, I doubt "a lot" are fake, but some do seem to be.

>>crazyg+Fr
> That is literally what the article describes, though, in Papua New Guinea.

No it doesn't. It says the UN came up with a different estimate, which the UN wound up not adopting. There is no evidence that the UN estimate actually used better methods.

> I do think the headline exaggerates, I doubt "a lot" are fake, but some do seem to be.

I am strictly arguing against "a lot" being fake, and specifically that an isolated example is not evidence of "a lot."

>>jjk166+801
The author brought up more examples besides PNG:

* Afghanistan

* Nigeria

* Congo

* South Sudan

* Eritrea

* Chad

* Somalia

* South Africa

Enough that "a lot" seems to be a fair characterization.

Also - while he implies this, I think it's important to mention explicitly - there's obvious fakery in the number of significant digits. If the numbers are approximations to the nearest ten million (or worse), it's a form of scientific fraud to provide a number like "94.9 million".