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How To Lie With Numbers 2 - Laffer Nonsense From The WSJ

by JakeS Wed Oct 24th, 2007 at 06:22:16 AM EST

Scatterplots and curve fitting

The versatility of scatterplots makes them one of the preferred methods of graphical representation of data. That same versatility also makes it the next best thing to impossible to provide hard and fast guidelines for how to spot phony scatterplots, because a lot of the techniques that are favoured by cranks also have legitimate uses in other cases (or even for the same kind of purpose but employed in another fashion). Therefore, this discussion will be both somewhat looser in structure than the rest of the series and rather heavier on theory. That is regrettable, but I do not believe that there is any viable alternative.

There are, however, plenty of examples of cranks abusing scatterplots to provide examples (and entertainment) throughout the section. I already provided a cursory analysis of why the scatterplot made by Svenskt Näringsliv back in part one was less than fully convincing, but I am going to leave that at a back burner for now, and share with you a delicious piece of nonsense that was floating around the blogosphere back in July:

[editor's note, by Migeru] Graph moved to the side to save whitespace on the Front Page

According to this blog entry, [T]he above figure actually appeared on the editorial page of the Wall Street Journal.

Diary rescue by Migeru


This graph is so manifestly absurd that I don't believe that it would actually fool any of my readers. Nevertheless, I will analyze it in some detail, to show why it's wrong. This is not only an exercise in poking fun at the WSJ, though there is certainly an element of that to it as well; the main point of the exercise will be illustrate some general principles with regard to curve fitting.

First off, I should note that displaying a curve in the same plot as a data set can serve two general (honest) purposes: It can either be a model that is independent of the data displayed and is provided for comparison or evaluation, or a model that is fitted to the data. Examples of the former include such things as theoretical computations of expected experimental results.

In academic writing, it is considered highly sloppy not to identify the origin of curves, but apparently newsies - or at least WSJ editorial newsies - do not feel particularly constrained by this custom. When no indication of the curve's origin is given, it is usually assumed to be some kind of fit to the data at hand - which also seems to be the case here, judging from the fact that the data point for Norway is exactly on the curve.

But failure to correctly identify the nature and origin of curves is just sloppy - it does not nearly rise to the level of dishonesty that justifies using a graph as an example of dishonest treatment of data. No, what's wrong with this curve is that it gives the impression of being a fit to the data, while it is anything but! There are many and more ways to fit curves to data, but all of them have one thing in common: The resulting curve should be somewhere in the vicinity of the data that it is fitted against.

For most kinds of fitting the fit should be above roughly half of the data points and below the rest. Furthermore, the points that lie above or below the curve should be fairly uniformly distributed across the length of the sample (e.g. a curve is usually a bad fit if all the points in the left-hand side of the plot are below the curve and all the points in the right-hand side are above, even if half the points are below and half above in total). It should be noted that while some experiments are sufficiently precise to follow theory almost exactly, resulting in a great number of points that are precisely on the fitted line, this is rare even in physics and I have never seen any examples of this outside that field.

This curve manifestly does not obey any of these rules - in fact it is off by so much that I doubt that the bozos at the American Enterprise Institute who made it even used a fitting algorithm to draw it!

Alright, that was the bad, now let's move on to the ugly.

This guy apparently thinks that he's found a Laffer curve in the data after all. Now, this is supposed to be mainly a review of curve fitting techniques, not a fisking of the WSJ's dishonest graph, but as it happens, The Englishman illustrates both a legitimate data analysis technique and a less permissible way of treating data.

The basic idea behind this graph

is to average the data in a number of intervals, in order to smooth out the plot. This is an entirely valid way of doing business, and can often be very useful.

So far so good. The averaging is done by eye, which is sloppy, but not inherently wrong. Unfortunately, he makes a major mistake when he connects the resulting averages by line segments and declares that there is a maximum. Grouping data is a way of simplifying your data set, but as a rule of thumb, if this kind of connect-the-dots exercise he's engaging in here is not justified before you compress the data, it's probably not justified afterwards either.

What he should have done was fit some curve to his new data. Unfortunately for him, it is not exactly clear what function describes the Laffer curve, so the best he could have done was try some low-order polynomials to at least get a handle on the behaviour of his data. I have done precisely that below, but that is rather beside the point of this diary.

Conclusions:

When you see a curve plotted in the same figure as a set of data points, you should note the following:

Is it a theoretical curve or a fitted curve? - if it doesn't say, assume that it's a fit.*

Fits should have roughly half the data points above and half below - furthermore, the data points that are above and below the curve should be distributed in the same way along the x axis.

Grouping data into intervals of one variable and averaging the other is usually a valid way of simplifying your data set

Connect-the-dots is usually not a valid way of fitting data - this is one of the most commonly seen honest mistakes.

Don't get cocky - this guide is not by any stretch of the imagination a complete overview of data analysis or curve fitting - people can and do write entire books on this subject. What I have presented here is a few rules of thumb, but the reader is encouraged to exercise caution in applying them, even more so than for the other diaries in this series.

*Note that in some cases the curve will be the result of a combination of fit and simulation - such is the case for climate models, for instance, but this is usually noted in the nearby text.

Previous conclusions:

Beware of bar graphs - if someone tells you that X causes Y and presents you with bar graphs, scrutinize them carefully. The proper graph to show correlation is in most cases a scatterplot. If he's using something else, chances are he's trying to pull a fast one on you.

Especially beware of highlighting - I'm sure highlighting single data points has legitimate uses, but off the top of my head, I cannot think of a single one. A very good indication that Someone Is Up To No Good.

Bar graphs are properly used to compare quantities - (naturally, such quantities as are compared must be comparable). This makes them particularly useful to present the results of polls, surveys and elections.

That someone isn't lying doesn't mean he isn't wrong - just because you can't catch someone red-handed in manipulating data is no excuse to disengage your other critical thinking processes.

The Entire Series:

How To Lie With Numbers - a short guide to politics and other things - introduction - bar graphs - highlighting.

ow To Lie With Numbers 1½ - more bar graphs - a cautionary note

How To Lie With Numbers 2 - Laffer Nonsense From The WSJ - scatterplots- fitting methods - data grouping

An Aside: Giving the AEI the benefit of the doubt for the moment, I can actually think of one kind of fit that would create such a silly curve: If you have very strong theoretical (or, in the case of the AEI, political) reasons to expect that the maximum or minimum of the y variable has some known relationship with the x variable - in other words, if you think you know the general shape of the curve that all data points will lie either above or below, you may draw an envelope curve - a curve that obeys your prior knowledge of the function's shape and envelopes all the data points as closely as your assumed function permits.

Example of envelope curves. harmonic.dat is simulated movement of a mass suspended in a spring. f and g are theoretical (i.e. not fitted) envelope curves. (The image apparently did not take kindly to being rescaled - click on image for original version.)

There are two general problems with such curves, and three that are specific to this particular curve: The general problems are that such an envelope is highly sensitive to outliers, and that such an envelope will always be provisional (in precisely the same way that the 'oldest fossil of species X' will always be provisional - if an older fossil of species X shows up, the previous oldest fossil will immediately cease being the oldest fossil).

The specific problems with this particular envelope curve is first of all that it isn't - some values that are clearly not outliers are clearly above the curve, and some values that are clearly not outliers are clearly below the curve. So if the curve is supposed to be an envelope curve, it's the worst example I've ever seen. The second problem is that as an envelope curve, it makes no sense, because it intersects with the x-axis at a point somewhere between 31 % and 32 %! In other words, if this were to be a theoretical maximum revenue curve, it would be impossible to collect revenue from a corporate tax rate above 32 percent! This is clearly nonsense.

The third problem with assuming that it is an envelope curve is that the AEI hack who drew it up tells us that it's supposed to be a Laffer curve - and the Laffer curve isn't supposed to be an envelope curve at all. At least it isn't in voodoo-economics-land - if it were, one could argue that one could increase revenue by moving up on the plot (i.e. by more efficient tax collection) as well as moving to the left (i.e. towards lower tax rates), and that would sort of blow the whole Reaganomics nonsense out of the water, wouldn't it... (Personally I suspect that the only way a Laffer curve makes sense is as an envelope, and even then I think it's overly simplistic.)

Another Aside: I decided, for the sake of the experiment, to take my own shot at curve fitting. To do that, I first read the coordinates of each data point off the original WSJ graph and turn them into a data file. This of course leads to a certain inaccuracy in the values, but as we shall shortly see that will not be a problem in the end.

After that I use GNUPLOT 4.2.2 to fit three different functions to the data:

The red curve is a second-order polynomial, the blue curve is a first-order polynomial, and the green curve is a first-order polynomial with forced intercept in (0,0).

Now, I don't know about you, but none of these babies looks particularly convincing to me, so let's take The Englishman's lead and fit to grouped data. In order to get a decent number of points in each group, I decided to use intervals of ten percentage points to group the data. The result:

Well, that didn't make us a whole lot wiser. It looks like the first-order polynomial with forced intercept is a bad fit, but let's see what happens when we add error bars to our data:

The error bars in this graph were computed by dividing the y value of each point by the square root of the number of data points that went into making it.

Now we see that all three functions are actually plausible (even if my error bar estimate was pessimistic by a factor of two - i.e. the error bars were twice as big as they should be - all three curves would still be within two error bars of all the data points). Back to square one.

We could, of course, continue to fit increasingly more complicated functions to our data, but unless we have some sort of theoretical underpinning for doing that, it would be largely meaningless.

My personal conclusion is that the best fit to the data in question is the neo-Laffer curve.

[Edited to add:] The above approach to error bars is rather quick and dirty, but it usually gives at least a rough idea about whether you have enough data to say something meaningful. Being less fatigued today (and a bit concerned about the hypocrisy of lambasting other people for sloppiness while engaging in sloppy curve fitting myself) than when I originally published the diary yesterday, I decided to revisit these errorbars and do them properly. The data points in the new plot below are computed by averaging both the x value and the y value (yesterday I averaged only the y value) in the intervals. The error bars are computed by taking the standard deviation of the averaged numbers and dividing by the square root of the number of data points that went into averaging. While I was at it, I also reran the fitting in light of the new error estimates, and the curves you see in the graph are the result of the new fits. (This gives a better estimate of the error bars and the fit.)

As you see, the new approach, while it has the advantage of not being sloppy, does not substantially change the conclusions previously drawn.

- Jake

Display:
Note that I have excluded Norway from all my own fits - AFAIK GNUPLOT uses a fitting algorithm that's rather sensitive to outliers, so I decided to exclude it. There exist more robust fitting algorithms, but they are usually a pain to implement correctly.

- Jake

Friends come and go. Enemies accumulate.

by JakeS (JangoSierra 'at' gmail 'dot' com) on Thu Oct 18th, 2007 at 09:21:24 AM EST
What! You believe a blog when it attributes a total absurdity to the Wall Street Journal? I'd suspect that this is another blogland rumour, too good to be true. To make a charge like this credible, there should be a link to the WSJ editorial itself, with the graph and everything.

Like this: The WSG editorial itself, with the graph and everything.

And no, the date is July 13, 2007 (page A12), not April 1st. What a bunch of loons!

Words and ideas I offer here may be used freely and without attribution.

by technopolitical on Wed Oct 24th, 2007 at 02:06:25 PM EST
[ Parent ]
Yeah, I know it's sloppy. My bad. Thanks for the link, too.

- Jake

Friends come and go. Enemies accumulate.

by JakeS (JangoSierra 'at' gmail 'dot' com) on Wed Oct 24th, 2007 at 04:15:53 PM EST
[ Parent ]
I would NEVER believe a blog which would claim that one WSJ Editorial was not completely loontastic. Never.

A 'centrist' is someone who's neither on the left, nor on the left.
by nicta (nico@altiva․fr) on Thu Oct 25th, 2007 at 03:10:03 PM EST
[ Parent ]
You're not going to get any sense out of the American Enterprise Institute. They get paid to do this kind of thing.

Martin Garnder's rebuff is excellent. I wish it were better known.

As an aside - interesting data point for the UAE. How do they run a government without collecting taxes?

by ThatBritGuy (thatbritguy (at) googlemail.com) on Thu Oct 18th, 2007 at 11:12:13 AM EST
by the stormy present (stormypresent aaaaaaat gmail etc) on Thu Oct 18th, 2007 at 11:30:59 AM EST
[ Parent ]
Hence cries of "no representation without taxation".

-----
sapere aude
by Number 6 on Mon Oct 22nd, 2007 at 05:57:07 AM EST
[ Parent ]
You're not going to get any sense out of the American Enterprise Institute. They get paid to do this kind of thing.

As an aside - interesting data point for the UAE. How do they run a government without collecting taxes?

Of course I'm not going to get any sense out of AEI. They're paid liars. I knew that from the outset. For that matter, I have - shall we say - an institutional mistrust of editorial pages in general and the WSJ editorial page in particular. But my objective here was and is not to document the many silly things that AEI or WSJ do. It is to illustrate how to spot cooked-up numbers, and that requires that I extend the assumption of innocence to whomever I'm fisking that day.

Regarding the U.A.E., there are three points to keep in mind: First, the U.A.E. is a (con)federation, and it is quite possible that their corporate taxes are applied at the national level. Secondly it's a minor country and minor country economies do funny things sometimes, because they can finance their entire economy off - say - oil extraction or banking services.

Third, the diagram doesn't show total tax revenue, it shows revenue from corporate taxes as a percentage of GDP (always pay attention to what's on the axes of a plot - that's probably the lesson for next time :-P). No, I don't think that makes sense in the context of a Laffer curve, but the entire purpose of this series is to offer tools that allow the reader to debunk number nonsense without necessarily knowing what the numbers represent. Lambasting the curve for not being a Laffer curve, while appropriate in the context of an ordinary fisking, would be beside the point in the context of this series.

- Jake

Friends come and go. Enemies accumulate.

by JakeS (JangoSierra 'at' gmail 'dot' com) on Thu Oct 18th, 2007 at 07:40:22 PM EST
[ Parent ]
Aside of all other issues, corporate tax rates are not equivalent with the effective rate of company taxation (in any way). Could well be that Norway has a higher effective tax rate than the USA, if there are less deductions and subsidies.
by nanne (zwaerdenmaecker@gmail.com) on Thu Oct 18th, 2007 at 12:10:31 PM EST
Kevin Drum confirms:
That is, it would be shocking unless you knew that the effective corporate tax rate in America isn't 35%, it's about 26%

Then there are of course things like the difference in regulatory environments, industrial policy, etc. etc. that make it impossible to make a sensible comparison of the tax policy of so many countries. Too many independent variables to isolate.

But this scatterplot does not even represent what the laffer curve is supposed to measure.

by nanne (zwaerdenmaecker@gmail.com) on Thu Oct 18th, 2007 at 12:17:31 PM EST
[ Parent ]
I didn't even read this story. I looked at the first chart, laughed, and now I'm going to go do some work-work.

you are the media you consume.

by MillMan (millguy at gmail) on Thu Oct 18th, 2007 at 04:24:28 PM EST
your advice on bar graphs is working already.

although I can't find the bar graph that set me off yesterday. The graph was a graph of US casualties that showed a dramatic decrease in casualties for last couple of months following a steady rise for the rest of the year. comentators were pointing out that the casualties for last month were about half that of the peak and using this as evidence of the success of the current surge.

Looking at it, I more instantly thought, hmmm, bar graph, they're pulling a fast one. The obvious choice was the graph had been chosen to start in January. I thought, wonder if the weather a year ago had any effect, so went to have a look at the figures, and no I was wrong, but looking back further, the half casualties are still at the top edge of those figures from further back than a year.

2005    107    58    35    52    80    78    54    85    49    96    84    68
2006    62    55    31    76    69    61    43    65    72    106    70    112
2007    83    81    81    104    126    101    78    84    65    24    0    0


Any idiot can face a crisis - it's day to day living that wears you out.
by ceebs (ceebs (at) eurotrib (dot) com) on Thu Oct 18th, 2007 at 08:22:44 PM EST
I'd be very interested in seeing that graph, if you can dig it out. That being said, from what you've presented so far it doesn't actually sound like the fault was the data format (bar graphs strike me as an entirely appropriate way of displaying that kind of data - it's a comparison, which is what bar graphs are good for remember?) so much as the data range. Leaving out inconvenient parts of the data set is another truly classic way of scamming with numbers. (As an aside, truncating data sets has always struck me as something of an exercise in black magic even at the best of times, and that's when everyone involved is being honest!)

The way these guys you're talking about picked out a single month for comparison, however, is precisely the kind of dishonest highlighting I was talking about in the first installment. When doing data analysis, you are not permitted to pick and choose single points and compare them to other single points or to peaks or whatever, because every data set has outliers and random fluctuations, and there is zero guarantee that the point you decide to pick is actually representative of anything.

A couple of general things to keep in mind with the casualty data from Vietraq is that it fluctuates somewhat from month to month (that's why I'd prefer to use a scatterplot rather than a bar graph myself) and it also depends on operational posture, number of troops in the field, etc. It seems like a reasonable proxy for how well the war is going, but one should be careful not to take it too far - after all, the Americans could simply sit in their compounds and get everything they need by air, and casualties would plummet. They would also, however, lose the war that way (to any extend that it isn't already lost, that is).

I'm tired and going to bed now, but I may return tomorrow (well, later today, technically) with some plots of data on Vietraq casualties over time.

- Jake

Friends come and go. Enemies accumulate.

by JakeS (JangoSierra 'at' gmail 'dot' com) on Thu Oct 18th, 2007 at 09:27:44 PM EST
[ Parent ]
I don't know about you, but to me this looks like an upwards trend:

FWIW, when GNUPLOT fits a first order polynomial to these data, it gives an upwards slope that is more than two asymptotic standard errors greater than zero. While this is not a proper statistical significance analysis, it does show that calling it an upwards trend is not grossly misleading.

- Jake

Friends come and go. Enemies accumulate.

by JakeS (JangoSierra 'at' gmail 'dot' com) on Fri Oct 19th, 2007 at 09:50:26 AM EST
[ Parent ]
Oh, forgot to tell what's on the axes and where I got the data: x axis is the month, with the first full month after invasion being labeled 1 and excluding the first and last month of war since they are incomplete. The y axis is the number of casualties during that month.

Data source.

Apologies for the double post.

- Jake

Friends come and go. Enemies accumulate.

by JakeS (JangoSierra 'at' gmail 'dot' com) on Fri Oct 19th, 2007 at 09:54:05 AM EST
[ Parent ]
I'd have some trouble fitting a downwards curve to that data ....

Day by day data might draw an interesting curve.

by Colman (colman at eurotrib.com) on Fri Oct 19th, 2007 at 10:01:14 AM EST
[ Parent ]
I'd have some trouble fitting a downwards curve to that data ....

Not if you were with the American Enterprise Institute :-P

Day by day data might draw an interesting curve.

Nah, random scatter would obscure any trend if you go to that resoluation.

I tried running two- and three-month running averages, but that doesn't make things a lot prettier, so I decided to just go ahead an post the raw data. This leads me to believe that a resolution of about one month (maybe you could push it down to two weeks, or even one week, but I don't think you could go much further) is about optimal as far as grouping goes.

Of course, one could use running averages (i.e. have a data point for each day that represents the average deaths pr. day for the 30-day period leading up to the point). But I'm not convinced that there is sufficient additional information to be obtained by doing so to justify the bother.

- Jake

Friends come and go. Enemies accumulate.

by JakeS (JangoSierra 'at' gmail 'dot' com) on Fri Oct 19th, 2007 at 10:47:51 AM EST
[ Parent ]

Not saying it's meaningful though ;-)

the subtle mathematical technique used is called drawing a random line that shows the message you intend  and bluffing competence.  I think it's the same technique used in the original graph in the story.

Any idiot can face a crisis - it's day to day living that wears you out.

by ceebs (ceebs (at) eurotrib (dot) com) on Fri Oct 19th, 2007 at 10:59:16 AM EST
[ Parent ]
In the previous plot of casualties from Vietraq, I accidentally only displayed data going up to month 35 (but the fit was from the full data set). Full plot here:

Unfortunately, the trend only becomes clearer if you include all the data...

Apologies all around for lack of proofreading.

- Jake

Friends come and go. Enemies accumulate.

by JakeS (JangoSierra 'at' gmail 'dot' com) on Fri Oct 19th, 2007 at 11:03:53 AM EST
[ Parent ]
Is that the same data set from iCasualties.org that this graph uses?

Iraqi Deaths By Year (iCasualties.org as of 2007/10/24)

Truth unfolds in time through a communal process.

by marco on Wed Oct 24th, 2007 at 09:06:13 AM EST
[ Parent ]
It's the same source, so If they use their own data, it is. But their graph only goes back two and a half years, and I disagree with the way they've presented the data (not that it's wrong, but I'm not sure it's particularly meaningful to do month-by-month comparisons - you'd normally do that if you thought that there was a reproducible pattern that depended on time of year, and it doesn't look that way to me), so I made my own to cover the entire war and occupation.

- Jake

Friends come and go. Enemies accumulate.

by JakeS (JangoSierra 'at' gmail 'dot' com) on Wed Oct 24th, 2007 at 03:49:02 PM EST
[ Parent ]
It was posted by bruno-ken in  yesterday's Salon.

by afew (afew(a in a circle)eurotrib_dot_com) on Fri Oct 19th, 2007 at 03:13:29 AM EST
[ Parent ]
Thanks for that, I knew I'd seen it somewhere, but it was a complete blank as to where.

Any idiot can face a crisis - it's day to day living that wears you out.
by ceebs (ceebs (at) eurotrib (dot) com) on Fri Oct 19th, 2007 at 03:15:07 AM EST
[ Parent ]
As I surmised, the issue here is not with the use of bar graphs but with dishonest highlighting (of course the dishonest highlighting in question was made possible by the use of bar graphs) and dishonest truncation of data.

- Jake

Friends come and go. Enemies accumulate.

by JakeS (JangoSierra 'at' gmail 'dot' com) on Fri Oct 19th, 2007 at 07:52:32 AM EST
[ Parent ]
This graph is better:

http://icasualties.org/oif/IraqiDeathsByYear.aspx

(I could not figure out how to insert it as a static image file.)

Truth unfolds in time through a communal process.

by marco on Fri Oct 19th, 2007 at 07:18:02 AM EST
[ Parent ]
I don't know about that, fhe figures for Iraqui casualties are I think too unreliable, and have been messed around with too much to produce any useful data.we have groups in differnt areas producing casulaty figures thatsuggest the differing success of their own internal factors.

I think the only probably reasonably accurate figures are those for US casualties, as its hard to hide bodies turning up on the home front.

Any idiot can face a crisis - it's day to day living that wears you out.

by ceebs (ceebs (at) eurotrib (dot) com) on Fri Oct 19th, 2007 at 08:49:50 AM EST
[ Parent ]
I agree, coalition regulars are the only variable counted in any resonably fair manner. This plot does:


(Click for details)

The bars are week by week casualties and the blue line is a four week moving average. It might look a bit of to the right because it is the average of the current week and the three weeks preceding this week. Following the blue line we can see that it has been an extended higher level of violence starting around september 2006. We also see that the level of violence varies greatly and picking four relatively calm weeks in september 2007 as proof of anything is highly dubious.

Sweden's finest (and perhaps only) collaborative, leftist e-newspaper Synapze.se

by A swedish kind of death on Sat Oct 20th, 2007 at 12:25:47 PM EST
[ Parent ]
Wev'e been talking a lot about the presentation of data without a lot of discussion about data collection methods. Granted, that's not the topic at hand, but once we get into Iraq deaths, we reallyneed to remember that it's fantasyland.

The collection of data for Iraqi and US deaths is about as fraudulent as one could imagine- what the tame Iraqis have done, along with the PCA is to delete large pieces of data and massage the rest, thereby cooking the books.- such as deaths by IED,(gone) and, in  the case of Civilian deaths, redefining criminal vs. sectarian deaths by preposterous criteria- like whether they got shot in the front of the head vs. the back of the head.

Garbage in- garbage out.

Capitalism searches out the darkest corners of human potential, and mainlines them.

by geezer in Paris (risico at wanadoo(flypoop)fr) on Wed Oct 24th, 2007 at 07:57:01 AM EST
[ Parent ]
...and is one of the hardest things for end-users of news media to detect.

It is possible to write entire books on the subject of data collection, and I decided that it was beyond the scope of this guide to include it - especially considering the fact that many of the techniques to detect doctored data acquisition require that you get your hands on the primary sources, which is a lot of bother for a newspaper aticle.

And often hacks will employ both bad data and bad presentation. It's usually easier to nail them on the presentation side of things...

But you're certainly right that any total figure for Iraqi casualties less than half a million or so is pure fiction. The official numbers certainly are. By at least an order of magnitude.

- Jake

Friends come and go. Enemies accumulate.

by JakeS (JangoSierra 'at' gmail 'dot' com) on Wed Oct 24th, 2007 at 04:02:40 PM EST
[ Parent ]
Sometime back I read, or heard, the US military isn't counting military deaths occurring outside Iraq in with the Iraqi count.  This little practice means a soldier who is severely wounded in Iraq but latter dies in a hospital in Germany, say, isn't included.

I have not been able to verify this.

She believed in nothing; only her skepticism kept her from being an atheist. -- Jean-Paul Sartre

by ATinNM on Thu Oct 25th, 2007 at 12:57:00 AM EST
[ Parent ]
That is certainly interesting, if true.

OTOH, soldier deaths are useful (to the extent that deaths can be useful...) primarily as a proxy for how things are going in general. So it does not really matter whether they lie a bit about the real numbers, as long as they've been lying in the same way since the war started.

The absolute values of coalition fatality figures from Vietraq are suspect anyway due to the fairly widespread employment of mercenary militias by the Coalition, as their numbers do not count towards casualties when they get killed.

- Jake

Friends come and go. Enemies accumulate.

by JakeS (JangoSierra 'at' gmail 'dot' com) on Thu Oct 25th, 2007 at 08:19:03 AM EST
[ Parent ]
Yep, eight out of the past ten months have been above the 34-month average. With October not over yet, make that eight out of nine.
(13 September to 13 October was Ramadan.)

-----
sapere aude
by Number 6 on Mon Oct 22nd, 2007 at 06:44:02 AM EST
[ Parent ]
I posted this in your other diary, but I was wondering what you think of Hans Rosling's approach to representing statistical data:

What do you think of Hans Rosling, his GapMinder software, and his two presentations at TED:

Debunking third-world myths with the best stats you've ever seen

New insights on poverty and life around the world

?



Truth unfolds in time through a communal process.
by marco on Wed Oct 24th, 2007 at 08:35:47 AM EST
I've never seen it before, so I'll refrain from commenting on it at the moment. It looks very interesting, though and I'll certainly take a look at it sometime.

But for the moment I've got an exam coming up that I'd really rather prefer to pass, and I'm going to make another diary about lying with numbers before I get around to Rosling - the Iraqi casualty presentation that ceebs dug out is too good (bad) to pass up.

- Jake

Friends come and go. Enemies accumulate.

by JakeS (JangoSierra 'at' gmail 'dot' com) on Wed Oct 24th, 2007 at 04:10:23 PM EST
[ Parent ]


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