I dont understand the post about error percentages, bell curve analysis, and probability. To me, it seems like a weak attempt at an excuse for publishing bad numbers.  You say you're not "wrong" if you publish 600k and VGC says 485k. That's a 24% error, and a difference of 115k units! How is that even acceptable? As mentioned, it only gets worse as numbers scale higher. A 20% error of 2M is 400k units. That is definitely not meaningless. You're validation for this is that NPD has a margin of error as well, and does the same thing that VGC does? Naturally there's error, but it's going to be much smaller.

And when I look at actual charts, if you want to say the numbers can't be wrong and you're just using probabilities, why are you even publishing these ridiculously precise numbers. What's the difference between saying one game sold 238,854 and another selling 241,913? Heck, what's the point of even publishing the high figures this site does if you're saying they can fall in such a large range? It doesn't take much thought to know a game like GTA is going to sell in the multi-millions. If you're saying the numbers can't be wrong because they fall within a decent portion of a standard distribution, despite being off by a couple million, what's the point?

Bye then.

To answer you and Carl directly - our lifetime hardware data is obviously a special case as we get shipment data and can make regular adjustments to our formulas (to represent changing trends as buying habits change over time) and correct older data to stay within a certain range of the shipment data. There is obviously increased emphasis on the lifetime hardware data as it means so much to everyone in the "console wars" and as such it is regularly adjusted to be as accurate as possible but if shipment data wasn't released then we would probably be looking a +/-5% range - which would be acceptable for most normal uses. Let's be honest here - the only reason people care is because the PS3 and 360 sales are quite close, if there was a 20-30m gap then nobody would be moaning about 200k here and there

As for official shipment data - none of us know exact stock levels, exact date ranges used and so on. Manufacturers have been known to play tricks in the past with their numbers to make them look better in one way or another so even these figures shouldn't be taken as gospel. I try to use shipment data as a guide to make sure we are in the right area but with stock being anything from 100k to maybe 2-3m at different points in time and with discrepancies in actual dates used etc then the shipment data is only useful to a certain point. With all of that said - what is the specific PS3 situation that you are refering to, I will take a look into it.

To make a general point - we don't just go in and start changing numbers to match someone else. I don't trust what some "insider" posts on Twitter and neither should anyone else. Similarly, most news sites that publish or leak data usually have no idea what they are actually talking about and often mis-report figures or don't compare the correct time ranges and so on. We have our own data, our own contacts, sources, insiders and I prefer to trust our info as I know where it is coming from and exactly what it represents.

It is quite infuriating when someone insists that our data is wrong because so-and-so said such-and-such when we have 100 times as much information on the subject than they ever will!

It's not an excuse, it is an explanation. Read my last post before this one - we take data from a sample population and scale it up to represent data from the whole population. Given variances in what the sample does compared to the whole population, there will be a bell-curve probability of the real values around our estimated one. The further you go from the estimate, the less likely you are to get that value.

Roughly speaking for the USA, we are using data from ~2m people to represent what the entire population are doing. Now a sample of 2 million people is enormous but even so it is less than 1% of the entire population and if for some reason we have bias towards particular regions, ethnic groups, age ranges, household incomes, genders and so on then our data will be an imperfect sample.

As for publishing data to the nearest unit - that is common practice. 238,854 doesn't mean that we have personally tracked exactly 238,854 sales of something - it means in reality that we may have tracked 1571 sales of something and via various scaling methods and adjustments have arrived at that figure as our best estimate of the sales of that product - which represents the centre of the bell curve.