Michael Eriksson's Blog

A Swede in Germany

Posts Tagged ‘statistics

The Woozle effect

with 4 comments

One of my main beefs with feminists is the abuse of statistics (there is “lies, damned lies, and statistics”—and then there is feminism), often in combination with the principle that a lie repeated often enough is eventually taken to be the truth. (For instance, I have repeatedly written about the 77 cents on the dollar lie.) As I have also observed in the past, if their claims actually were true, anyone with a brain and a heart would be a feminist, myself included—but they simply are not true.

Recently, I became aware that there was a name for (a subset) of this type of abuse: The Woozle effect, where a piece of (real or merely claimed) information is repeated and repeated, with less and less constraint, until even an originally true claim is turned into an outrageous lie. (Cf. also the “Chinese whispers” game.)

I would strongly encourage the readers to read at least a part of the (lengthy) page behind the link, which includes not only a discussion of the phenomenon in general, but also discusses a number of common feminist “statistics”, including in the area of domestic violence and rape, as well as some uses not necessarily related to feminism. To quote one example:

  • Gelles conducted a study using police domestic disturbance reports as the source. He explained this very specifically as a way to locate clear examples of domestic abuse. 20 Families with known histories were found. There were 20 Families referred by a private Social Service agency, making 40 in total. Then as a control group, neighbours of these families were recruited, making 80 families in total where half had a known history of Domestic Abuse. He was not looking for a national or global sample. Gelles says “Of the eighty families, 55 percent reported one instance of conjugal violence in the marriage. This was not unexpected, since half of the couples were selected because we thought they might be violent.” The evidence is for a small group, selected only due to police reports and known incidents. The 55% also referred to both men and women as victims.
  • Straus writing the forward to the book “The violent home” used the 55% figure but without qualifying it.
  • Langley & levy then cited Gelles & Straus claiming “Estimates that 50 percent of all American wives are battered women are not uncommon”. Gelles & Straus made no such claim or inference in their work.
  • Langley & levy, journalist writing a book, then applied the Woozle to the general population arriving at the figure of 28 to 36 million American Wives being battered annually. “The twenty-six to thirty million are roughly half of all married women.”.
  • The 28 Million figure, published in the book “Wife beating: the silent crisis”, then received extensive media coverage, including claims that at least 7 other studies showed the same 28 Million figure to be valid. In accounting for the lack of previous knowledge of what was called “A conspiracy of silence by men” the US Government, Congress, The American Bar Association, police and FBI, were all referred to as having “Culpable Ignorance”.

And please: The next time someone makes a statistical claim, please stop to consider whether it actually is true, given in the correct context, interpreted reasonably, and carries the appropriate qualifications about the circumstances. Sometimes a healthy skepticism is all that is warranted; sometimes the claim is simply absurd and can be ruled out with a little own thought, as with claims of absurd proportions of false accusations of rape to unreported rape cases or the existence of 14 million child-porn websites; sometimes the interpretation turns out be highly dubious, as with boys and girls doing housework ; sometimes the difference between correlation and causation is not even remotely understood; …


Written by michaeleriksson

November 11, 2017 at 12:28 am

Interpreting statistics and research (housework among boys and girls)

with 2 comments

I just encountered a Swedish news service claiming that “Girls help [do housework] more at home”/“Flickor hjälper till mer hemma”.

While this article, to my mild surprise, did not make the usual partisan statements of e.g. “Girls are better at X”, it still manages to show some common problems with reading of statistics and how poor critical thinking can lead people (in particular, journalists) astray.

To quote relevant parts:

SCB har undersökt vilka hushålls-
sysslor barn i åldrarna 10-18 år
hjälper till med.

83 procent av flickorna och 79 pro-
cent av pojkarna hjälper till med hus-
hållsarbete minst en timme i veckan.

([“The bureau of statistics”] has investigated what household chores children in the age range 10–18 years help with.

83 per cent of the girls and 79 per cent of the boys help with household work for at least an hour a week.)

Syssla; Flickor; Pojkar

Bäddar sin säng; 82 proc; 77 proc

Diskar eller plockar i/ur diskmaskinen; 81 proc; 71 proc

Städar sitt rum; 78 proc; 64 proc

Tar hand om syskon; 35 proc; 36 proc

Arbetar utomhus; 23 proc; 40 proc

(Task; Girls; Boys

Makes own bed; 82 %; 77 %

Does the dishes or loads/unloads the dish-washer; 81 %; 71 %

Cleans own room; 78 %; 64 %

Takes care of siblings; 35 %; 36 %

Works outdoors; 23 %; 40 %)

(The news service in questione does not provide an archive, so I cannot give a permanent link. Should I encounter the data from another source, I will add one.)

Going by the numbers presented (but beware that the full report may give another view; for instance, the list of task is likely to be abbreviated), the claim is highly dubious. Firstly, the difference in overall numbers is comparatively small (certainly not large enough to allow for predictions about individuals) and, depending on the size of the sample, could lack statistical significance. Secondly, and more importantly, the tasks are oddly chosen:

Both making ones own bed and cleaning ones own room are things that do not constitute “helping at home”—they are something that a child in the age bracket given either does or does not do for his/her own benefit. (Similarly, baking cookies for ones own consumption is not “helping at home” either—nor is tweaking ones own moped.)

The natural step would be to adjust the overall numbers by removing these entries. For lack of in-depth data, this is not possible; however, we can make a very rough first comparison by simply adding percentages. Now, in the original version we have 82 + 81 + 78 + 35 + 23 = 299 for the girls and 77 + 71 + 64 + 36 + 40 = 288 for the boys. (Pleasingly, 288 / 299 * 83 is just shy of 80, which compares well to the original 79 % overall for boys—in particular, as rounding can cause minor distortions.) Removing the “self serving” tasks, we instead have 81 + 35 + 23 = 139 for the girls and 71 + 36 + 40 = 147 for the boys—who are now ahead by more than they used to trail (as a proportion of the total).

The tentative conclusion, then: Boys (!) help more at home. (Incidentally and anecdotally: This was definitely the case when looking at me and my sister as teenagers. She could barely be bothered to put her own plates in the dish-washer; I moved the lawn and chopped wood for the fireplace.) Of course, I cannot guarantee that this would remain true if the raw data was re-investigated, but the gap is sufficiently large that the original claim (that girls help more) should be viewed as unsupported.

As an aside, the removed categories reflect an issue that is worth keeping in mind when discussing housework: Men and women have different priorities when it comes to cleaning and use of available time. (In my opinion, men have it the right way around and women should take a more relaxed attitude.)

Written by michaeleriksson

June 19, 2011 at 2:06 pm

On correlation, causality, and related issues

with 2 comments

The previous entry touched upon the question of fallacies. Recently, I have been involved in a Swedish discussion of povertye, which has put emphasis on some of my concerns.

Notably, there seems to be a considerable lack of understanding of questions such as causality vs. correlation, how scientific studies work, and similar. Annoyingly, this problem is very common, even among journalists and politicians (who should know better as a professional requirement)—and, horrifyingly, even the odd scientist.

Let us first look at the concepts of causality and correlation:

Correlation implies that there is a connection of some kind between two phenomena, personal characteristics, or similar—but is says nothing about how the connection works. In particular, it does not say that the one is the cause of the other, or the reverse; and it is quite common that a third something is the cause of both, or that they are partially mutually causing each other. (Technical use of “predicts” is similar: It too is not a causality, but unlike a correlation it can be a one-way street. If I pick out a random person on the street here in Cologne, there is a fair chance that he is German—the first predicts the second, with a high probability of correctness. On the other hand, picking a random German, the chance that he is on a street in Cologne is comparatively small—the second does not predict the first.)

Causality, OTOH, catches just this causing.

To take a few examples of how these concepts can work (and easily be misunderstood):

  1. Height and weight are reasonably strongly correlated. However, which is the cause of the other? An increase in height does (on average—a qualifier that I will leave out below) cause an increase in weight, because there is more body present. However, an increase in weight can also often cause an increase in height: Lack of nutrition can stunt growth and those who eat more are likely to both gain weight through fat/muscle gain and to gain height through a lesser risk/degree of malnutrition. In addition, entirely other factors can cause both weight and height gains (e.g., strictly hypothetically, that those genetically predisposed to tallness are also predisposed to obesity).

    Here we see a complex interaction of factors. We can further note that, although height and weight are correlated, the correlation is imperfect: An obese 5-footer can be heavier than skinny 7-footer. Correlations only rarely allow for predictions about individuals, and instead find their use where aggregates are concerned.

  2. Assume that we consider a large sample of men and women, with and without bikes (and that sex and the possession of a bike are independent of each other). Looking specifically at the three subsets women (X), bike-owners (Y), and female bike-owners (Z), we find that membership of X and membership of Z correlate: Being a woman increases the chance of being a female bike-owner and being a female bike-owner necessitates being a woman. In the same way, membership in Y and membership in Z correlate.

    It would now seem plausible to assume that since both X–Z and Z–Y correlate, then we would also have a correlation X–Y. That, however, is not true! There is (in this model) no connection whatsoever between X (being a woman) and Y (owning a bike).

    Here we see the risk of “chaining” correlations.

  3. Consider the set X of all Finns and the set of Y all people with Finnish as their native language. Clearly, X and Y have a strong correlation. It would now, on a too casual glance, seem plausible that the same applies to any subset of X. However, there are specific subsets which have no or even a negative correlation—notably, the large minority of Swedish descent.

    What is true for a set is not (necessarily) true for all subsets. (Including, obviously, individual cases, which can be mapped to sets with one member.)

  4. Consider a school class with blond and brown-haired children. The teacher (for reasons of his own) gives the blond children an apple and a chocolate bar, while the brown-haired are given an orange and bag of wine-gummy.

    Assuming that no other edibles are present (and that the children are not extremely voracious…), there are perfect correlations among the children between owning an apple and owning a chocolate bar, an orange and wine-gummy, being blond and owning an apple, and so on. There are also perfect negative correlations between e.g. apple-owning and orange-owning (not all correlations need indicate a connection of X -> Y, but they can also be of the X -> not-Y kind).

    However, there is no causation between apples and chocolate bars or between oranges and wine-gummy. (One of the main rules of science: Correlation does not imply causation.)

    Looking at e.g. being blond and owning an apple, we land in a complicated situation: On the one hand, we could argue that the blond hair did cause possession of the apple; on the other, this could be seen as a spurious thought because the actual cause behind the correlations is the teacher. (What is a causation and what not is often a far from clear decision, and care must be taken when basing decisions on ambiguous causations. In a similar vein, there are often causes and underlying causes.)

  5. Assume the same setting as the previous example, when a second teacher rushes in, confiscates all candies and replaces them with fruit (the bastard!), so that all children have exactly one apple and one orange.

    Here we see an oddity: Causation does not need to imply correlation.

    The first teachers actions did cause the students to be given candies, but the actions of the second nullified that effect. Similarly, the first teacher did cause the children to be given fruit according to a certain pattern, but this pattern (in the sample at hand) disappeared with the actions of the second teacher (without nullifying the actions of the first teacher).

  6. A (hypothetical) study of the NBA is made, with the result that the correlation between height and prowess (by some measures) is low, zero, or even negative.

    Does this imply that height has no effect on prowess? No–here we have the classic issue of a pre-filtered sample. Studying NBA players reduces the variation of ability to a very high degree (compared to the overall population) and the variation of height (to some degree). This makes the sample flawed (for many purposes) and the conclusion invalid.

    Repeating the same study on the overall population, without this pre-filtering, will show a large positive correlation.

    A correlation is only as good as the samples used (in general) and using samples which are “top heavy” (in particular) can hide correlations that actually are present.

  7. Similar to the above, other variations of highly flawed conclusions based on flawed samples can be constructed, e.g. by creating statistics on car accidents for the overall population based on a sample of hospital visitors; by using a conclusion which is true for one population, but not for another; or by making comparisons between samples that may be inherently unequal, e.g. by trying to measure a difference in hockey-ability between Swedes and Canadians by comparing random samples of NHL players. (The entry barrier to the NHL will be lower for a Canadian, which means that the Swedish sample will have undergone a stronger pre-filtering.)

An important conclusion from the above is that if a scientific study claims that “X and Y correlate” (or “X predicts Y”), great care should be taken before assuming a causation or suggesting new policy. In fact, even if the study actually does make claims about causality, great care should be taken: The scientist(s) may be sloppy, driven by ideological motivation or research grants, or seeing what the result “should” be (rather than what it is)—scientists are only human.

The last point is one of importance: Many non-scientists have a somewhat superstitious take on scientists, and assume that they master all complexities in they encounter, take all aspects of a problem into consideration, and so on. This is simply not a correct estimate: Even when a scientist is aware of all aspects (unlikely, bordering on a tautological impossibility), he will still be forced to make simplifications. A social-science study, e.g., may pick out a handful of variables of relevance, try to catch any remaining issues in a generic error term—and then proceed to test these on a sample that is too small, picked with imperfect randomness, or otherwise deviating from the ideals. (This not to mention the many other complications that can occur with flawed measurements, leading questionnaires, whatnot.)


As has subsequently occurred to me, the above examples can be somewhat misleading in that they are mostly “binary” (someone has/is something—or not). This was a deliberate choice to have simple and easy to understand examples; however, it is important to bear in mind that the typical practical case will be of a different character. The first item, dealing with height and weight, is a good example: There is no binary “tall implies heavy”, “short implies light”, but a a gradual increase of expected/average weight as height increases (and vice versa).

This is particularly important when I speak of “negative correlation” above: This should not really be seen as the presense of X implying the absense of Y, but as a decrease of Y as X increases. A good example is speed and travel time: If a vehicle goes faster (all other factors equal) the time taken for it to reach its destination decreases.

Written by michaeleriksson

July 26, 2010 at 5:52 pm