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10% of All X's Account for 25% of All Y's:

I often see these sorts of statistics that purport to show that some fraction of the X's are disproportionately prone to event Y. One paper I read, for instance, reported that 10% of all police officers in a department account for 25% of all abuse complaints, and used that as evidence for the proposition that some police officers are especially prone to misbehavior. One can imagine similar claims when, say, 10% of all holes on a golf course account for 25% of all hole-in-ones, or 10% of all slot machines account for 25% of all jackpots, and so on.

The trouble is that this data point, standing alone, is entirely consistent with the X's being equally prone to Y. Even if, for instance, all the holes on a golf course are equally difficult (or all the police officers equally prone to abuse complaints), and hole-in-ones (or complaints) are entirely randomly distributed across all holes (or officers), one can easily see the 10%/25% distribution, or 20%/80% distribution, or whatever else.

Consider a boundary case: Say that each police officer has a 10% chance of having a complaint this year. Then, on average 10% of all officers will have 100% of this year's complaints. Likewise, say that each police officer has a 1% chance of having a complaint each year for 10 years, and the probabilities are independent from year to year (since complaints are entirely random, and all the officers are equally prone to them). Then, on average 9.5% (1 - 0.99^10) of all police officers will have 100% of the complaints over the 10 years, since 0.99^10 of the officers will have no complaints.

Or consider a less boundary case, where the math is still easily intuitive. Say that you have 100 honest coins, each 50% likely to turn up heads and tails. You toss each coin twice. On average,

  • 25 of the coins will come up heads twice, accounting for 50 heads.

  • 50 of the coins will come up heads once and tails once, accounting for 50 heads.

  • 25 of the coins will come up tails twice, accounting for no heads.

This means that 25% of the coins account for 50% of the heads — but because of randomness, not because some particular coins are more likely to turn up heads than others.

Likewise, we see the same in slightly more complicated models. Say that each police officer has a 10% chance of having a complaint each year, and we're looking at results over 10 years. Then 7% of all officers will have 3 or more complaints (that's SUM (10-choose-i x 0.1^i x 0.9^(10-i)) as i goes from 3 to 10). But those 7% will account for 22.5% of all complaints (that's SUM (10-choose-i x 0.1^i x 0.9^(10-i) x i) as i goes from 3 to 10). And again this is so even though each officer is equally likely to get a complaint in any year.

Now of course it seems very likely that in fact some officers are more prone to complaints than others. My point is simply that this conclusion can't flow from our observation of the 10%/25% disparity, or 7%/22.5% disparity, or even a 20%/80% disparity. We can reasonably believe it for other reasons (such as our knowledge of human nature), but not because of that disparity, because that disparity is entirely consistent with a model in which all officers are equally prone to complaints.

If you have more data, that data can indeed support the disproportionate-propensity conclusion. For instance if nearly the same group of officers lead the complaint tallies each year (or nearly the same group of slot machines leads the payouts two months running), that's generally not consistent with the random model I describe. Likewise, if you have more statistics of some other sort — for instance, if you know what the complaint rate per officer is, and can look at that together with the "X% of all officers yield Y% of the complaints" numbers — that too could be inconsistent with a random distribution.

But often we hear just a "10% of all X's account for 25% of all Y's" report, or some such, and are asked to infer from there that those 10% have a disproportionate propensity to Y. And that inference is not sound, because these numbers can easily be reached even if everyone's propensity is equal.

UPDATE: (1) Some commenters suggested this phenomenon "depends on the sample size; if the sample size is large enough, the inference is sound." That's not quite right, I think.

The sample size in the sense of the number of police officers / golf holes / coins does not affect the result. I could give my coin example, where 25% of all coins yield 50% of all heads, with a million tosses.

The sample size in the sense of the number of intervals during which an event can happen (e.g., the length of time the officers are on the force, if in the model there's a certain probability of a complaint each year) does affect the result. But if the probability per interval is low enough, we can see this result even when there are many intervals.

Say, for instance, that there's a 1% chance of a complaint per month for each officer, and we look at 240 months (representing an average police career of 20 years). Then even when all officers have precisely the same propensity to draw a complaint, 9.5% of all officers would have 5 or more complaints, and would account for over 21.5% of all complaints. So a 9.5%/21.5% split would be consistent with all officers having an identical propensity to generate complaints, even with a "sample size" of 240 intervals. If the monthly complaint probability was 0.005, then 12% of all officers would account for over 33% of all complaints.

(2) More broadly, this isn't a matter of "sample size" in the sense that we'd use the term when discussing significance testing, and talking about "statistical significance" wouldn't be that helpful, I think. If you have a lot of data points, you can determine whether some difference between two sets of results over those data points is statistically significant. But here I'm talking about people's drawing inferences from one piece of (aggregated) data -- 10% of all X's account for 25% of all Y's. Statistical significance testing is not apt here.

Mongoose388:
Did they separate out cops not usually encountering the public on a daily basis? Also, how many of the complaints are found later to be justified?
Common sense would tell us that some officers are more often than others in circumstances that could lead to complaints of abuse. The cop sitting at the desk all day and not out on patrol will have less encounters with the public in hostile situations. The cop on patrol will have more opportunities to be accused.
3.17.2008 2:46pm
Brian Mac:
Gotta love stats. I seem to remember Sowell making a similar argument, in the context of discrimination (I forget in which book).
3.17.2008 2:49pm
NI:
This reminds me of the old joke about the testing being done on medicine for a chicken disease. One-third of chickens treated died, one-third recovered, and the third chicken ran away so no conclusions could be drawn.

Or take the drunk who wanted to know why he was waking up every morning with a hangover. He kept careful records of what he drank as follows:

Monday: Scotch and soda, result: hangover
Tuesday, whiskey and soda, result: hangover
Wednesday, rye and soda, result: hangover
Thursday, vodka and soda, result: hangover

Conclusion: Soda causes hangovers.
3.17.2008 2:51pm
Richard Nieporent (mail):
25 of the coins will come up tails twice, accounting for 25 heads

I suggest you fix the typo. [Whoops, fixed, thanks! -EV]
3.17.2008 3:07pm
statfan (mail):
Next week, will you address statistics of the form: This is the Xest year in the last Y years?
3.17.2008 3:14pm
J. F. Thomas (mail):
The trouble is that this data point, standing alone, is entirely consistent with the X's being equally prone to Y.

And it would be reckless to use this statistic alone. I doubt that it is. (And of course your slot machine example is exactly correct since the house randomly and frequently reprograms slot machines to pay out at better odds--a single machine might be paying out 2 cents on the dollar one day and 12 cents the next day).
3.17.2008 3:18pm
Mike S.:
I remember reading (in a major national news source) sometime ago (I think in the 1980's) that some small fraction of seniors accounted for a large fraction of Medicare payouts; the author thought we should do something about it. My reaction: big surprise, health insurance spends most of the money on those people who get seriously ill. And this is a problem because ...?
3.17.2008 3:42pm
BABH (mail):
How about "the wealthiest 10% of Americans pay 70.8% of all income taxes"?
3.17.2008 3:43pm
Zathras (mail):
There is an important commonality in all the theoretical examples cited. The expected value of the number of hits attributable to an individual coin (= #heads), police officer (= #complaints), etc., is small to none in every example.

Suppose, instead, that the expected number of complaints (=total #complaints/#officers) is higher (maybe 20). In this case, the 10/25 realization would indeed be significant. What is the expected number of complaints in the paper referred to?
3.17.2008 3:44pm
RI Lawyer:
Excellent post. Reinforces my belief that high schools should teach basic probability to students so the kids will have some knowledge basis to evaluate whether the claims directed to them, whether by marketers, public officials or candidates, are meaningful.
3.17.2008 3:45pm
Zathras (mail):
For example, in the case of the coins, in the limit of large number of flips, a straightforward application of the Central Limit Theorem gives that x% of the heads come from x% of the coins.
3.17.2008 3:48pm
Eugene Volokh (www):
BABH: Nope, that's quite different, for a variety of reasons (that the 10% are not just any old 10% but 10% that share a certain trait, much the same group ends up paying most of the taxes each year, and more).

Zathras: That's the problem -- the cited study didn't give any other data points that could lead us to reject the randomness hypothesis.
3.17.2008 3:49pm
George Weiss (mail) (www):
this is a great post EV
3.17.2008 4:12pm
Ariel:
It will also depend on the volume of police officers and complaints. As the number of either or both increases, the meaningfulness of the statistic also increases. I've used Pareto curves in the context of a large number of Customer Service Representatives (CSRs). We were trying to analyze cheating behavior and were able to come up with a metric of % of cheats in a certain type of interaction. We excluded CSRs with fewer than a certain number of interactions, since they would not be meaningful. But with thousands of CSRs and tens or hundreds of thousands of cheating events, we were able to show that 1% of CSRs were causing 10% of the problems, and 10% causing 50%.

The percentages standing alone are not meaningful, but they can be if they also talk numbers. Incidentally, it's this same type of statistic that is widely used in the Gini curve to look at distribution of wealth and income. While there are problems with that analysis (chiefly that it is a static picture and does not account for movement between groups - problems that EV pointed out for this case as well), it can provide a static picture which can be combined with a dynamic understanding of events as well.
3.17.2008 4:12pm
Ted Frank (www):
I made a similar point in the medical malpractice context, where we regularly hear that some small x% of doctors are responsible for y% of malpractice verdicts.
3.17.2008 4:41pm
bob (www):
Next you'll be telling us that 4-2 win in the World Series or a 14 pt win in the Super Bowl could be explained by random chance rather than dominance. When will it end?
3.17.2008 5:02pm
bob (www):
Next you'll be telling us that sports results could be explained by random chance rather than extraordinary demonstrations of skill. Where will the madness end?
3.17.2008 5:04pm
TomH (mail):
Next, bob will give another sports analogy. How will it end?

(sorry bob, couldn't resist)
3.17.2008 5:16pm
AF:
As Zathras pointed out, the soundness of the 10%/25% inference depends on the sample size; if the sample size is large enough, the inference is sound.

For this reason I disagree with Professor Volokh that the 10%/25% formulation, without more, is necessarily misleading. It is only misleading when the sample size is too small to support the inference, or when it is unclear whether the sample size is large enough to support the inference. Sometimes it is obvious that the sample size is large enough, and in those cases it is unnecessary to include the sample size or any other data.

For example, it would be valid for a journalist to write that 10% of major league baseball players account for 25% of all home runs, in order to show that some players are better at hitting home runs than others. She would not have to include further data, because most baseball fans know the approximate number of games and at-bats in a baseball season, and the underlying statistical significance is intuitively obvious.
3.17.2008 5:40pm
q:

And it would be reckless to use this statistic alone. I doubt that it is.

Whether or not it is used alone, the statistic is irrelevant, since it neither supports nor refutes the proposition. Given that it's likely to make some readers more supportive of the proposition, it's misleading (I wouldn't go so far as to say reckless, but its use is certainly troubling).
3.17.2008 5:44pm
John McCall (mail):
Zathras: but that's in the limit case, which is not an actual instance. In any single observed history, it is highly unlikely that the results will appear to be evenly distributed; and this remains true even for large (but still finite) N.
3.17.2008 5:48pm
JerryH (mail):
How is the flipping of coins comparable to police officers? Coins are neither intelligent, have free will or have attitudes. Police do. It is entirely possible that 10% of the police have an attitude that causes them to choose to be more abusive than the other 90%.
3.17.2008 5:59pm
AF:
John McCall: It is extraordinarily unlikely that 10% of a set of fair coins will account for 25% of heads, where each coin is flipped an equal and large (but still finite) number of times.
3.17.2008 5:59pm
Steven Joyce (mail):
Zarthras's limit argument is correct for the case in which the number of coins is fixed while the number of flips per coin tends to infinity. It does not hold if the number of coins goes to infinity while the number of flips per coin is held constant, or more generally, if the ratio of the number of coins to the number of flips per coin tends to infinity.

In most of the examples we've considered, the number of observations per individual is small relative to the number of individuals, which means that the limit argument almost certainly does not apply, even approximately.
3.17.2008 6:04pm
Richard Nieporent (mail):
Do you realize that 40% of absences from work occur on Monday and Friday.**

**Yes I stole that from Scott Adams.

I recommend the book Innumeracy by John Allen Paulos that discusses the inability of the general public to understand basic statistics and the way that they misapprehend risk and misinterpret data.
3.17.2008 6:09pm
TQ:
Prof. Volokh, I'm not persuaded. Your setup assumes that the important feature is either getting or not getting a complaint, and treats this as a Bernoulli event -- either you did or didn't (in a given year), just as a coin either comes up heads or doesn't. But I don't think that's the appropriate model, since it treats a 'one-time offender' who gets a single complaint in a given year the same as a 'repeat offender' who gets 10, 20 or 30 complaints.

A Poisson process may be better. This models the probability that an officer gets a given number of complaints (possibly zero) in a year, and so contribute multiple times to the overall complaint counter. And that more closely matches the language of "25% of all complaints" (rather than "25% of those with complaints lodged against them").
3.17.2008 6:10pm
SenatorX (mail):
I agree with the commentors about sample size because it seems to me that is the important factor. Doesn't the standard deviation shrink as sample size increases? It would be nice if people would stick to 4% deviation before reporting anything.
3.17.2008 6:25pm
jdege (mail):
The number I keep seeing is how 2% of gun dealers are responsible for 80% of gun traces.

My question is what percentage of all gun sales are by those same 2% of gun dealers?

Most gun dealers only sell a handful of guns in a year. The majority of gun sales are by a small number of high-volume gun dealers. (The same is true for pretty much everything - remember Pareto's law).

It's impossible to judge whether there those dealers represent a problem, without the additional information that the media never seems to report.

Why is that? Bias? Or simple laziness?
3.17.2008 6:39pm
Westie:
Prof. Volokh,
The problem with your counterargument is that people (and golf courses) aren't coins. That is, common sense teaches that people are not identical; some of them are miscreants, and some are not. Likewise, the holes on a golf course are not identical; some of them have more hazards than others. Thus, based on his or her common sense and common experience, a person reading the relevant statistic (10% of officers are responsible for 25% of the complaints) will rightly assume that there is a non-random distribution of complaints, and that the distribution is likely to stay that way even as the number of complaints increases.
In your example, any given coin is equally likely to populate the "2 coins give 2 heads" class; in real life, not every officer is equally likely to populate the "receives more complaints" class.
3.17.2008 6:43pm
Eugene Volokh (www):
JerryH: As I said, "Now of course it seems very likely that in fact some officers are more prone to complaints than others. My point is simply that this conclusion can't flow from our observation of the 10%/25% disparity, or 7%/22.5% disparity, or even a 20%/80% disparity. We can reasonably believe it for other reasons (such as our knowledge of human nature), but not because of that disparity, because that disparity is entirely consistent with a model in which all officers are equally prone to complaints."

SenatorX: The sample size in the sense of the number of police officers / golf holes / coins does not affect the result. I could give my coin example, where 25% of all coins yield 50% of all heads, with a million tosses.

The sample size in the sense of the number of intervals during which an event can happen (e.g., the length of time the officers are on the force, if in the model there's a certain probability of a complaint each year) does affect the result. But if the probability per interval is low enough, we can see this result even when there are many intervals.

Say, for instance, that there's a 1% chance of a complaint per month for each officer, and we look at 240 months (representing an average police career of 20 years). Then even with an entirely random model, 9.5% of all officers would have 5 or more complaints, yet they would account for over 21.5% of all complaints. So a 9.5%/21.5% split would be consistent with all officers having an identical propensity to generate complaints, even with 240 intervals to work from. If the monthly complaint probability was 0.005, then 12% of all officers would account for over 33% of all complaints.

As I mentioned in my response to JerryH, we could still infer that some police officers do have a higher propensity to generate complaints than others do. But we can't assume that from the 9.5%/21.5% split, or a 12%/33% split, or whatever else. Only if we have more data (e.g., evidence that the same officers tend to have the most complaints every year) would we be able to draw a sensible inference from that data.
3.17.2008 6:50pm
Student:
Or simple ignorance. I've never understood reporters' (or for that matter lawyers') blind confidence that they can speak intelligently when they know nothing at all about the topic.
3.17.2008 7:05pm
ReaderY:
A lot of people have made a lot of money out of the fact that, assuming one investment strategy is just as good as another, about 3% will do better than average 5 years in a row. As they say, past performance is no guarantee of future results.

Of course, that's nothing compared to the money lawyers can make. If there are a hundred different brands of detergent, people using about 3% of the brands will have an above-average cancer rate five years in a row. And about one in a thousand will have it ten years in a row. Same with obstetricians and birth defects.
3.17.2008 7:09pm
John McCall (mail):
AF: given a long enough history relative to the probability of a single success, that's true. On the other hand, the top 10% will certainly produce more than 10% of successes. For example: in a simulation of 250 batters with 600 at-bats apiece and a 1% chance of hitting a home run, 5.6% of batters hit 10.5% of home runs, while 15.6% of batters hit 25.3% of home runs. My point is that the convergence of these distributions to the limit case is not very quick at all.

Westie: your common sense assumes its conclusion. That is, you notice differences and assume they're significant of something, when that's not always the case. I would agree that this is accurately describes normal human behavior, but its prevalence doesn't make it any better-considered.
3.17.2008 7:19pm
SenatorX (mail):
Thanks for the response EV and I think I understand what you are saying now. I was thinking about the longer service time to narrow down offenders but that's not the point. You are showing more the folly of probabilities in general. It's more important to extract the probabilities from the data(and take them with a grain of salt) than to try and cast the probabilities down onto the subjects. Isn't there some famous quote about statistics and lies?
3.17.2008 7:35pm
Eugene Volokh (www):
SenatorX: I'm not quite sure I grasp your point, but I'm pretty sure my criticism doesn't apply to use of statistics generally. My point is simply that one particular sort of statistical argument, which is based on reasoning from one aggregate datum, is not sound.
3.17.2008 7:48pm
Curt Fischer:
Three comments:

i) Great post Prof. V! If I were in charge of things, statistics would be taught much more extensively and early in high school and college than it currently is.

ii) The examples (coins and police officers) all involve sums or averages of binomially distributed variables. There is a small sample size problem that the commenters started to zero in on, and that Prof. V. addressed somewhat in the update. But I think the plainest way to say it is that formulating a meaningful, relevant "x% of X accounts for y% of Y" statistic would involve individual estimation of the binomial probability p for each member of the population. In other words, adding more police officers or coins to our data set isn't going to help. We need more flips of each coin or more years observing each officer. If we individually estimate p (probability of success/failure) for each coin or officer, standard statistical tests can tell you how likely it is that the p values would arise by chance.

iii) Another way to eyeball whether "x% of X accounts for y% of Y" stastics make sense for a given data set is just to construct the Lorenz curve for the data. This curve shows how y varies with x, so you can read off a "x% of X accounts for y% of Y" for any value of x you want. If the Lorenz curve has hops, skips, or jumps, it's suggestive that you might not have enough data points to have confidence in your statistic. If it is relatively smooth, may be meaningful to invoke the statistic.
3.17.2008 8:27pm
Tony Tutins (mail):
Eugene Volokh, I'd like you to meet Vilfredo Pareto. Vilfredo Pareto, please meet Eugene Volokh.

I would put it this way: Some data is random and some has an assignable cause. Statistical outliers should be investigated. If ten percent of your staff is taking 80 percent of the sick days, you should probably have a talk with that ten percent. Rank order whatever data you're analyzing. Focus on the cops with the most complaints. Changing their approach to handling the public has the possibility of making the biggest reduction in complaints.
3.17.2008 9:36pm
PersonFromPorlock:
The thing about the normal curve is that it tells us that as the population increases, the age of the oldest living person does too; and that if the population increases infinitely, the oldest person lives forever.
3.17.2008 10:01pm
Curt Fischer:

PersonFromPorlock: normal curve


What normal curve? Who's talking about a normal curve?
3.17.2008 10:18pm
SenatorX (mail):
Well it seems to me statistics are used all the time in sly ways to "prove" peoples points. You may focus on one particular sort of statistical argument but it infers a problem with the use of statistics in other arguments as well.
3.17.2008 10:25pm
Eli Rabett (www):
As I recall, the authors of this blog are very into 10 percent of all criminals commit 25% of all crimes so lock them up forever.

However, as is usual with such things the original statement was very poorly put and merely shows that EV is astatistical.
3.17.2008 10:49pm
Safety Guy (mail):
Here's some data on occupational injuries and illnesses that I tabulated. I worked at a appliance manufactoring plant as a safety professoinal while in graduate school. The plant had a shared workforce of 3000 people, 1600 of which worked in my facility. For years, our plant of 1600 had the some of the worst injury rates in the company (I was only in the plant for 1 year as a co-op when I conducted this study). We tried everything to reduce injury rates (which were mostly soft-tissue arm and hand injuries). The injuries were very odd. We noticed that 2 shifts would have no problem performing a job but the third person would be constantly getting hurt. We found no patterns related to task, shift, supervisor, or anything that would drive us to a common root cause. We eventually started bringing in ergonomics experts to help... with no success.

On a guess, I decided to look for trends in the one place a safety professional never should look — the people. After pulling 5 years of injury and illness data, it was plain that for this group of 3000 people, only 6 percent of the population was responsible for 96% of the injuries. Shocked, I re-ran the numbers and the data still came out the same. Then I looked at how many of that 6% were in my building and, shockingly, I had 70% of them. As a co-op, I was the safety person responsible for the Second Shift. We were averaging a few injuries a week at that point — about the same as the other two shifts but with no patterns we could detect. Armed with my data (I shared it with my boss and another co-op who both dismissed it), I went out every night and personally spoke with every person on my list — I asked how they were doing, if they were having any issues with their jobs, about their kids... basically BS'd with them and I did it every night.

What happened next was dramatic. We went from having a few injuries per week to having none for my last three months in the facility (I left for a better job). The change occurred almost instantly once I started working my "list".

One other thing we noted — when we cross checked the "list" with FMLA data, that 6% accounted for 82% of the FMLA time and 91% of the lost work days for either work-related injury or personnel medical.

I've held a lot of safety roles since that job, each increasing in responsibility. And everytime I start a new job, I do a quick, discrete check of the historical data to see if this pattern is present. Fortunately, I've never found the pattern in the years since.
3.17.2008 10:51pm
E:
Brian Mac, are you referring to Howell?
3.18.2008 12:22am
not a cop:
I don't buy it. The problem is reducing a complicated human interaction problem down to a binomial random variable. The coin doesn't have any influence over the number of heads or tails, cops do have influence over the number of (valid) complaints they receive. Substitute the number of speeding tickets received by a speeder, or better yet, the number of felonies committed by a criminal, and you see that randomness in this context just really doesn't make sense. A cop has to earn a complaint, he/she doesn't just "receive" it.
3.18.2008 8:17am
AF:
Professor Volokh: thanks for the update. Of course, your point (1) is correct. We seem to be in agreement: sample size "in the sense of the number of intervals during which an event can happen" can affect whether the 10%/25% inference is sound.

I think your point (2) misses the mark. Journalism is not scholarship; the math should be correct, but the reporter doesn't have to "show his work." If the 10%/25% inference is in fact sound -- which it is for some populations -- it doesn't strike me as highly blameworthy for a reporter to draw that inference without providing additional data.

Interestingly, one of your examples -- holes in one-- comes close to illustrating my point. Holes in one are unusual so the 10%/25% inference may not be valid with respect to them. But with respect to more common occurrences like birdies, a 10%/25% distribution would very likely indicate significant differences among the difficulty of golf holes (holding constant the skill of the golfers). It would be nice to see the calculations, but I wouldn't blame a reporter for making this point without providing additional information.
3.18.2008 11:39am
FredR (mail):
I'd suggest you're using he wrong methodology altogether by using a gaussian distribution model instead of a power law distribution here. With a gaussian model most examples cluster in the center and drop off on the ends, while with a power law they cluster on one end and drop off steeply, typically with each iteration being about one-half of the last one. This is the so-called "long tail." In everyday terms it's often called the 80-20 rule, as in 20% of your customers cause 80% of your complaints, and so on. And as someone else mentioned, these are cops and not coins, meaning they have their own powers of decision.

An example of power law distribution can be seen by looking at the number of kills made by fighter pilots in WW I &II (where there are a large number of samples). A relatively small number of pilots accounted for most of the kills. As you move down the "tail," there are a large number of pilots with a few kills and many with none at all. Given the large number of examples and the time involved this cannot all be luck. Some pilots are just better -- a lot better -- than others, and it shows. For a much better explanation and more examples I recommend Chris Anderson's THE LONG TAIL and N. N. Taleb's THE BLACK SWAN.

Thus in your example, analyzing it with a gaussian model shows it as being random noise, while a power law model explains very well that there are a few bad cops who are causing the problems. Of course they may also be the most effective ones as well, but that's another problem. FWIW Malcolm Gladwell did an article (in the New Yorker, I think) showing that in prisons most complaints of brutality can be traced a small number of guards.

Also, for whatever reason, it looks like I've been readmitted to the fold if you are reading this.
3.18.2008 12:25pm
Zacharias (mail):
In the interests of political correctness, the three female scientists in this country account for half the appearances of "scientists" on shows like NatGeo, Animal Planet and Discovery.
3.18.2008 12:51pm
Curt Fischer:

I'd suggest you're using he wrong methodology altogether by using a gaussian distribution model instead of a power law distribution here.


I'm confused by FredR's comment, because I haven't noticed anyone talking about the Gaussian distribution to model anything. Well-developed statistical methods can determine how well an experimentally measured distribution (e.g. of coin flips or police complaints) fits to the equations for a power-law distribution, or a normal distribution. I don't think these methods are particularly relevant here.
3.18.2008 5:42pm
markm (mail):

not a cop:
... A cop has to earn a complaint, he/she doesn't just "receive" it.

Eugene was just using complaints against police as one example of a frequent phenomenon, but even there you're missing the point. I'm not a cop, but in every job involving public contact I've seen, there are earned complaints and there are undeserved complaints. I'd expect that some citizens just have unreasonable expectations of cops. I've known people that have an animus against cops in general that may be a motive for trying to get a cop in trouble, or that are willing to fabricate a case against a cop in a particular instance to make a political point. And some complaints are flat out lies by criminals trying to gain leverage against the department in hopes they can negotiate a reduction in charges.

Presumably the unjustified complaints will be randomly distributed across all the officers exposed to the situations where such complaints arise, while bad cops will repeatedly receive justified ones. So a way to test whether a large police department has a good process for sorting justified from unjustified complaints and disciplining or getting rid of bad cops is to compare the distribution of complaints across the department to what would be expected if they were all undeserved. The excess number of cops with many complaints then gives an estimate of how many bad cops there are, which can be compared to the number of disciplinary actions arising from complaints.
3.18.2008 9:56pm
Steve2:
I hate distributions, and accordingly statistics. They confuse and bewilder me. Well, not the binary distribution, I get that. On, off. 0, 1. Either, or. Other than that, though... Give me uniformity so I can tell what's going on!
3.18.2008 10:45pm
FredR (mail):
Curt Fischer: a coin toss analogy such as the one EV uses in his example assumes a gaussian distribution, because coins, unlike real world examples, don't do power laws. It's simply the wrong type of analysis for that particular problem.

Reminds me of E. O. Wilson's (an expert on ants) pithy evaluation of socialism. "Good idea, wrong species."
3.19.2008 12:07am
Curt Fischer:

FredR says: a coin toss analogy such as the one EV uses in his example assumes a gaussian distribution, because coins, unlike real world examples, don't do power laws. It's simply the wrong type of analysis for that particular problem.


Just asserting that you're right doesn't convince me. Why are power laws "right" and other distributions "wrong"? Keep in mind that in the OP, Prof. Volokh was simply making up his own hypotheticals to show how "x% of X accounts for y% of Y" statistics can be misleading.

What do power laws have to do with that? Where is your proof that Prof. Volokh's hypotheticals are invalid? Is there a Fundamental Theorem (or Very Large Incontravertible Data Set) of Cop Complaints Distributions or something that I don't know?

Also, I still don't see what an example with coins has to do with the Gaussian distribution. Coins are usually assumed to follow a binomial distribution.
3.19.2008 12:47am