11:46 PM
So I have neglected to take some pictures of my goban. I did not get my stones today either, I SHOULD be getting them tomorrow. That is if USPS can move a package 30 miles in a day. I did manage to find some usable stones, but there was only enough to play on the 9×9 side of the board. 19×19 tomorrow hopefully.

Now to the real reason I am here. This section was about the variability of characteristics that are unique or extraordinary in a given species. Darwin did mention that he was not able to find any evidence of this in plants, but they vary to a large degree anyway. But there are some specific characteristics that vary greatly in a given species. Some of the so called secondary sex characteristics also vary in such a degree. I don’t really feel like writing anymore.

I made caramel.

Did you hear the joke about the statistician who put her head in the oven and her feet in the refrigerator? She said, “On average, I feel just fine.” That’s the same message as in this more widely known joke about statisticians:

statistician drowning in a pond with an average depth of 3ft

(source)

And then there’s this one: did you know that the great majority of people have more than the average number of legs? It’s obvious, really: Among the 57 million people in Britain, there are probably 5,000 people who have only one leg. Therefore, the average number of legs is

And because most people have two legs, they have more legs than the average number of legs (1.9999123) (source). In this case, the median would be a better measure than the average or the mean.

But seriously now, averages can be very misleading, also in statistical work in the field of human rights. Take income data, for example. Income as such isn’t a human rights issue, but poverty is, as well as income inequality. When we look at income data, we may see that average income is rising. However, this may be due to extreme increases at the top 1% of income. If you then exclude the income increases of the top 1% of the population, the large majority of people may not experience rising income. Possible even the opposite. And rising average income – even excluding extremes at the top levels – is perfectly compatible with rising poverty for certain parts of the population.

Averages are often skewed by outliers. That is why it’s often necessary to remove outliers and calculate the averages without them. That will give you a better picture of the characteristics of the general population (the “real” average income evolution in my example). A simple way to neutralize outliers is to look at the median – the middle value of a series of values – rather than the average (or the mean).

An average (or a median for that matter) also doesn’t say anything about the extremes (or, in stat-speak, about the variability or dispersion of the population). A high average income level can hide extremely low and high income levels for certain parts of the population. So, for example, if you start to compare income levels across different countries, you’ll use the average income. Yet country A may have a lower average income than country B, but also lower levels of poverty than country B. That’s because the dispersion of income levels in country A is much smaller than in country B. The average in B is the result of adding together extremely low incomes (i.e. poverty) and extremely high incomes, whereas the average in A comes from the sum of incomes that are much more equal. From the point of view of poverty – which is a human rights issue – average income is misleading because it identifies country A as most poor, whereas in reality there are more poor people in country B. So when looking at averages, it’s always good to look at the standard deviation as well. SD is a measure of the dispersion around the mean.

More posts in this series.

Making the right decisions in the middle of an iteration or release cycle is risky and needs good judgment. Investing in a reusable asset is no exception.

What can help you take decisions on whether it is worth investing the time and effort for making a software capability reusable? The single most important factor is business relevance. Is the capability a priority for your business? Even if it is not a priority, is it one for the medium or long term? If the answer to these questions is no, you shouldn’t invest valuable resources into making your capability reusable. Yes, if it isn’t relevant, it needn’t be reusable. Assuming a capability is business relevant, there are several considerations you can think about:

Design Considerations:

1. Do you have user stories that either directly or indirectly requires this capability? For instance, if you have a login customer user story, and you don’t have a customer component, this is an opportunity to create one. In the same vein, the login customer story itself might have variations. What are the different kinds of login that you need to support? Username/password, username/password/special verification text, email/password, username/random set of questions, username/defined set of questions etc.
2. Do you understand the extent of variability that is required for the asset? Consider the ratio of the features that vary a lot vs. the overall functionality.  Even if you end up with a gross, high-level percentage it will help you during design.
3. Consider specifically the aspects that vary and the number of variations you need to support. This is very critical to plan for the reusable asset. Remember, you are pursuing iteration not perfection! Draw a distinction between the variations that are an absolute must vs. ones that can be built over time. If you look back at the first point, maybe your business only needs the simple login using username and password. In that case don’t rush into building in variation support. On the other hand, if they come back and ask for multiple kinds of login you can always refactor to support variability.

Process Considerations

1. Will the capability add significant schedule or technical risks? If so, you need to make this transparent and get clarity from your sponsors. You can explain the rationale for investing in a reusable capability if there is business relevance. Similarly, if schedule risk is unacceptable or you have other higher priority assets to build, refactor, etc. then add this asset to the refactoring list and revisit later.
2. Will the capability introduce deployment changes? Does it need an additional resource such as a database, a configuration file, or is dependent on an external service provider? In that case you will want to run it by your production support and operations partners and get their buy-in.

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My coordinator asked me,  “Have there been any surprises that you have observed or experienced in the classroom in general, in students, in yourself?” 

We get surprised when something happens differently than we expected.  One expectation I had is that a lesson plan has a lot of weight, and when applied to different classes of similar ability, would get similar student responses most of the time.  It seems that this has happened only about half the time between the two periods of 9^th and 10^th grade geometry classes.  The other half of the time, one or the other class is very inattentive and /or noisy, but it isn’t consistently one class.   I’m guessing that by planning for the diverse learner and applying it to the whole class, we would have our contingency plan for those days when the students have trouble settling down.

Another expectation I had is that a new building with new technologies would have more teachers using the technologies.  I’ve visited schools in older and newer buildings, and not noticed much difference in the use of technology.  So far, the document camera is the most frequently used technology.  I expect it’s been adopted well because teachers don’t have to change their processes much when using it.  I am interested in seeing how many teachers use Quizdoms, but it seems that only one out of a dozen teachers that have it uses it.

I’m amazed at how consistent my mentor seems to be between 2 periods of geometry, and how differently each class responds.  With each, she has the same materials prepared, the same questions are on the board, and she covers the lesson very similarly.  With 3^rd period, half the students were involved in side conversations, and were told several times to be quieter.  Considering they’re having a quiz next Monday, the side conversations made me wonder if some had already given up.  And it’s only the 3^rd week of school!  The 6^th period responded very differently.  Considering it was the last class on a Friday, and an early release day at that, I’d expected this typically social group to be as distracted as the 3^rd period.  Contrary to expectations, they were quiet and attentive, recognizing the importance of the review.  Wow!

My mentor’s style and techniques are very consistent, yet get very different responses class to class. I’ve wondered about various ways I’d try to get attention and quiet with noisy classes, and then observed her using these same techniques, to varying success.   Teaching is a personal thing, and what works with some students does not work with others.  

My mentor teacher had a parent conference with 2 other teachers and a disruptive student and his mom.  The teachers and the mom were all concerned, with the mom stating that she’s out of options and may need to send him to live with his father in another state.  Each of the 3 teachers had called home individually with concerns.  When I take a look at the student and what’s happening inside and outside our classroom, I get a better picture of the student, although still incomplete.  After hearing about the conference, I felt increased compassion toward this young man who’s been making poor choices. While he’s still expected to behave respectfully in class, I felt an increased interest in finding out what he needed to help him be successful.  The day after the conference, I didn’t see much change in him, but I did see a change in me.

There seems to be a built in resistance to looking at taking the variability out of training, learning or education because people are different and have different learning styles.  Some people are looking a variability as a good or necessary thing.  I think what people are confusing is the difference between variety and variability.

Here’s an example of the difference.  Let’s go to Baskin Robbins and see the 31 flavors of ice cream.  There’s something for everyone.  That’s variety.  However, if every time you order Chocolate it looks different and tastes different that’s variability which isn’t very desirable.

To accommodate different learning styles we can teach a sales process in different ways.  However, we need to teach the same sales process.  Variability often comes in when trainers decide to substitute content, processes or models with there own preferences.  As a result, people are trained differently.  This difference leads to work being done differently which makes it harder to manage and leads to more mistakes and lower performance.  It also happens when trainers aren’t up-t0-date on what’s actually happening in the work place.  It’s not uncommon for a process or procedure to change on the job but not in the training.

It also happens when people assigned to coach and mentor do things differently.  They pass along these differences and one shift does things one way and another shift does them another.  This means that best practices aren’t shared.  So I’d say, variety…good, variability…bad.

The unity of opposites is a philosophy that has been around for thousands of years. If an element exists, it must have an opposite. If there is hot, there must be cold. If there is good, there must be evil. If there is light, there must be darkness. One of the most interesting views on the unity of opposites is that the things we perceive to be opposites are really the same thing, just at different points along a spectrum. For example, on one end of the spectrum there is darkness and on the other there is light, with infinite degrees of variability in between. The simplest way to measure the balance of the spectrum is with percentages. If a weather forecaster tells you that there is a 70% chance that it will rain tomorrow, you know that there is also a 30% chance that it will not rain tomorrow. Our world is composed of opposites, and so are you. Because of the constant fluctuation of human thought, 100% of any one mindset is virtually impossible. Even if you are 99% good, you must also be 1% evil. Part of you loves. The other part hates. You are not one or the other. You are everything. Your strength of character depends on your ability to embrace your good side and allow evil to subside. “Taking the high road,” is one way to put it.

There’s an old Native American story you may have heard before. An elderly Cherokee man tells his grandchildren that all his life, there has been a fight inside his heart between a good wolf and a bad wolf. The good wolf represents peace, love, and happiness. The bad wolf represents anger, fear, and sorrow. His grandchildren ask him, “Which wolf wins?” The man answers, “The one I feed.”

Duality will always be a part of your life. The more you focus on one side of that duality, the more heavily weighted it will become. You can slide the marker back and forth along the spectrum at will. There is a part of you that loves to be challenged. There is a part of you that loves helping others. There is a part of you that knows you can be successful in life. Always embrace the side of yourself that serves you best. If you are 80% certain that you will fail, you are also 20% certain that you will succeed. Continue focusing on that 20% and it will begin to grow. One day, you will tip the balance in your favor.

Perhaps no recent article irritated me more and made me feel more vulnerable as a solar professional than James Schlesinger and Robert Hirsch’s Washington Post op-ed, “Getting Real on Wind and Solar” (April 24, 2009). The op-ed said we shouldn’t take solar and wind seriously, because they are unpredictable and cannot be turned on at will. There is a grain of truth to their statement. It is a serious criticism of wind and solar, and probably the most telling after the worst one – high cost. But I think it is because high cost is being overcome, especially in wind, that this next level of criticism is being raised. That’s progress.

I authored a response with Bundestag member Hans-Josef Fell, the co-author of the German feed-in tariff legislation (see Fell’s presentation at our kick-off symposium here). The Washington Post didn’t see fit to print it. Maybe they had not heard of the German feed-in tariff or its transformational importance to the global PV market.

I used to think James Schlesinger, the first Secretary of Energy, was an ally of solar since he was involved with the founding of SERI (now NREL), the place I worked for 26 years. Maybe I missed something in his biography. He is on the board of a coal company, now, so perhaps he’s changed his mind. I prefer to think he just hasn’t thought enough about it.

The main thing Schlesinger and Hirsch seem to overlook is that we can do a lot with intermittent electricity. Electricity is already intermittent, for example, since demand fluctuates as we individually turn things on and off. So dealing with fluctuations is not so off the wall as they might have you think. The question is: how big can the fluctuations be before they cause problems?

We don’t yet know the answer to that question. If anyone does, please post a comment. But wind, for example, is reaching penetration levels that are quite substantial in some places in Europe and even the US. We should be able to learn from that. Based on an initial reading, it seems that 20%-wind (wind kWh/total regional kWh) is acceptable. By that, I mean at an added cost in cents per kWh and extra carbon dioxide 20% wind is acceptable. So perhaps that much intermittency would cost an extra 10% in money and CO₂ emissions, though that is a rough estimate. The recent report “20% Wind in 2030” by the DOE is a good source for those curious about the details. More on this subject can be found on the Solar Institute’s Resources page – especially a NERC report on grid reliability. Essentially, we can back up intermittent sources with fast ramping natural gas, some smart grid demand-side management, and more effective use of solar and wind forecasting. These are not expensive.

Now to my ears, 20% doesn’t sound like a whole lot, but that might not be right. For example, 20% of our current electricity use would be about equal to all the energy we need to move all our light-duty vehicles in the US, if they were powered with electricity instead of oil. So if we pushed down on the accelerator and deployed solar and wind to the point of 20% of electricity, we could possibly eliminate all the oil we use for our cars and light trucks at the same time.  By doing that, we would also be eliminating the CO₂ from all that oil and replacing it with CO₂-free electricity, with say a 2% penalty (10% of the 20% overall offset). This would be nice for our energy self-sufficiency, to put it mildly.

So it seems that dismissing wind and solar as unworthy of being “real” as Schlesinger and Hirsch would have you believe, is, itself, rather unreal, or at least a bit alarmist.

While we are doing this, and converting our vehicles to electricity, we can also look into improved electric storage, so we can get further “real” with the rest of our electricity and energy needs after that. That would be about 2030.

Ken Zweibel

Here is the updated presentation for 2009, with more information on Excel and a worked set of examples with hummingbirds, to tie in with the natural selection topics.

And Geoff Browne kindly gave permission to upload his t-test powerpoint to slideshare:

Resources:

Updated Essential Biology 01 – Statistical Analysis (word)

Click4Biology statistical analysis page, with great help for calculators and excel

Excellent Handbook of Biological Statistics from John MacDonald

Sumanas statistics animations

Field Studies Council stats page, including the t-test

Open Door Website stats page and help with graphs and tables

Gapminder awesome human population stats tool

And this enlightening talk from Han Rosling: No More Boring Data!

-

Using your calculator:

- Using the TI GDC (from Click4Biology)

- Using the Casio pdf download (from keymath.com)

Commonality And Variability Analysis, bir projedeki varlıkların değişebilir mi yoksa benzer varlıklar mı olduğunun çözümlemesidir. Takdir edileceği üzere Commonality Analysis and Variability Analysis olmak üzere iki aşamadan oluşur:

Commonality Analysis: Benzerlik analizi (çözümleme) bir ailenin üyelerinin ne şekilde aynı olduğunu anlamamıza yardımcı olan araştırmadır

Benzerlikler bir projedeki temel kavramları tanımlar. Örneğin pencereden dışarı bakıldığında çam, gürgen, meşe olmak üzere üç çeşit ağaç görüldüğü kabul edilsin. Bu üç çeşit ağaç nasıl tanımlanabilir? Cevap oldukça basittir. Bu üç çeşit ağaç da sonuçta ağaçtır. Bununla birlikte bu üç çeşit de aynı görünüme sahip değillerdir ve fakat bu farklılık yüzünden dünya üzerindeki bütün ağaçlara ayrı ayrı isim verilmesi de yanlış olur. Bu durumda çam, gürgen, meşe gibi üç adet genellik kullanılabilir.

Aynı şekilde iki beyaz tahta kalemi ile dolmakalemin aynı olduğu durumlar sorulduğunda bunun yanıtı çok zor değildir. Ağaç örneğinde olduğu gibi kolayca her ikisinin de kalem veya her ikisinin de yazma aleti olduğu söylenebilir. Ancak gerçekte neye baktığımızı çözümlersek ve kendimize bunların en bilinen ortak yönlerini değil de aynı oldukları daha başka özelliklerini soracak olursak, ne yaptığımıza, hangi problemi çözdüğümüze bağlı olarak farklı yanıtlar elde ederiz. Diğer bir deyişle tanım kümemize(domain) bağlı olarak bu nesnelerin daha kullanışlı, daha işe yarar gruplamalarını elde edebiliriz.

Bu kalemlerin benzerlik çözümlemesinde yazma araçları olduklarını söyleyebiliriz. Bu aklımıza ilk olarak gelen benzerliktir. Ancak bu onların aynı olduğu tek durum değildir. Bunların plastik olduklarını söylebilirsiniz. Yine onların tek renkli olduklarını söyleyebilirsiniz, her ikiside siyah ve beyazdır. Her ikisinin sonsuza kadar kalıcı olmayan geçici nesneler olduğunu söylebilirsiniz. Her ikisininde ofis aracı olduğunu söyleyebilirsiniz. Her ikisini de ofis araçları satan bir dükkanda veya ofisinizin çekmecesinde bulabilirsiniz. Her ikisininde kapağı olduğunu söyleyebilirsiniz ama bunu farklı bir şekilde şöyle sorabilirsiniz. Bunlar neden kapağı olan eşyalardır? Bu sorunun yanıtı bu iki kalemin koruması olması gerektiği sonucunu ortaya çıkarır. Eğer kapaklarını açık bırakırsanız mürekkepleri kurayacak ve kullanılamaz hale gelecektir. Kapaklı olması gerektiği düşüncesinden hareketle bunların üç adımda kullanıldığını söyleyebilirsiniz. Açma adımı, beyaz tahta veya bir kağıt üzerine yazma veya çizme şeklinde kullanma adımı ve sonlandırma yani işlem tamamlandığında kapağın kapatılması adımı. Aynı zamanda bunların yuvarlak çubuk şeklinde olduğunu ve masanın kenarında bir çıkıntı yoksa kolayca yuvarlanıp yere düşebileceklerini söyleyebilirsiniz. Bu şekilde devam ederek sonsuz sayıda benzerlikler elde edebiliriz. Bizim için önemli olan çözmekte olduğumuz problem açısından bu benzerliklerden hangilerinin en fazla işe yaradığıdır.

Variability Analysis: Değişkenlik(variability) çözümlemesi bir sonraki adımdır.  İşimize en çok yarayacak benzerliği belirledikten sonra bu benzerliğin içinde bulunduğu çevrede(context) nasıl değiştiği sorulur. Benzerlik ile değişkenliği en iyi aşağıdaki cümle tanımlar.

Mimari bakış açısından benzerlik çözümlemesi bize oluşturduğumuz mimarinin  ne kadar uzun ömürlü olduğunu(longevity), değişkenlik çözümlemesi ise kullanıma ne derece uygun olduğunu gösterir.

Gender gap in maths driven by social factors, not biological differences (Not Exactly Rocket Science):

Since 1894, some scientists have suggested that men have a greater variability in intellectual ability than women, a simple statistical quirk that would result in more male prodigies. This was the controversial hypothesis that Lawrence Summers mentioned in his now-infamous speech at the National Bureau of Economic Research Conference in 2005:

[...]

To test that, Hyde looked at data from maths tests in Minnesota and compared the numbers of boys and girls who scored in the top 5% of their year. The ratio was 1.45, meaning that for every two girls in this elite group, there were around three boys. In the top 1%, the ratio was 2.06, meaning two boys for every girl. That seems to vindicate the Variability Hypothesis, but those figures only applied to white American children. In other ethnic groups or, indeed, in other countries, the picture was very different.

For Asian-Americans the ratio was actually 0.91, meaning more girls than boys in the top 1%. International studies have found similar trends. One analysis of tests from the Program for International Student Assessment (PISA) showed that 15-year-old girls matched or outnumbered their male peers in the top tiers within Iceland, Thailand and the UK. Two studies found that 15-year-old boys and girls were equally varied in their mathematical skills in most of the countries taking part in PISA and the Trends in International Mathematics and Science Study (TIMSS). In some, like the Netherlands, girls actually turned out to have the wider range of ability.

So much for the idea that a greater variation in ability underlies the larger number of men in the top ranks of mathematics – if that had any biological basis, it should apply to all populations regardless of ethnicity or nationality. Clearly, that’s not the case. Instead, the evidence suggests that whatever gender differences exist are mostly down to social factors.

There’s plenty of evidence to suggest that, given the right social environment, the gender disparity in maths becomes vanishingly narrow. Various studies have found that countries with the poorest degrees of gender equality also have the widest gulfs between male and female mathematical performance. And in their own analyses, Hyde and Mertz found that a country’s gender inequality gap significantly correlates with the ratio of boys to girls in the top 5% of PISA test scorers, and the proportion of girls competing in the International Mathematics Olympiad – an incredibly challenging competition where the top scorers have one-in-a-million ability.

It’s no coincidence that countries like Denmark, the Netherlands, the UK and Iceland, where equal numbers of girls and boys populate the top 1% of the PISA results, are also in the top dozen countries in terms of gender equality. (The US, for the curious among you, is ranked 31^st, between Estonia and Kazakhstan) These international comparisons point the finger at gender inequality, rather than greater male variability or aptitude, as the main reason behind the lack of women at the highest levels of maths in some countries.

Obviously, that includes a multitude of sins that will need to be addressed – lack of attention or encouragement, the effects of stereotypes, a lack of female role models, wilful misogyny and unconscious biases, hostile work environments, and so on. Addressing these issues is no easy task but at the very least, this review summarises firm evidence that attempts to do so will see female mathematicians rivalling their male counterparts at every level of the discipline.

Of course, it takes more than facts and empirical evidence to change the opinions of most male geeks about female inferiority in math. The Slashdot community’s reaction to the story is an example of this.

Slashdot’s reaction

Let us ignore the first comment joking that the study cannot be trusted because it was done by a team of female mathematicians/statisticians, as well as the comments assuming that women in CS are useful because they increase the chances of men in CS getting laid. In addition to submissions about women triggering general sexist comments, the study that disproves innate female inferiority in math becomes an opportunity for male geeks to rehash old, obsolete arguments about innate female inferiority in math.

Note that most of the Slashdot comments I repost below were modded up with a score of 5 (out of a maximum of 5).

“Girls are barely passing math. It doesn’t come naturally to them.”

In a comment titled “Taking vs Excelling”, modded up with “Score:5, Interesting”, Tanktalus writes:

Taking the course is not the same thing as passing or excelling. It’s an important metric, but not the only one. Perhaps we have a “traditionally disadvantaged” group being pushed, in the name of equality, into an area they dislike because it doesn’t come natural, and they’re barely passing. That’s not success – that’s a failure because these people probably would be more successful in life playing to their strengths rather than weaknesses.

In other words, this commenter didn’t bother to RTFA, but felt confident anyway in asserting that women are probably barely passing their math courses.

“Perhaps women are biologically inferior in math, and the appearance of equal ability is produced by cultural factors.”

In a comment titled “Just a thought”, modded up with “Score:5, Insightful”, Xeth writes:

(not meant to necessarily have any correlation with reality)

People seem to assume that what is happening is that previously, cultural norms dictated gender inequality when there was no biological basis, and now that those norms have changed, biological equality is restored. Couldn’t it be the other way around? I.e. that there is a biological inequality, that is being altered by cultural factors to produce equality?

Confronted with the study’s suggestion that women are not biologically inferior in math ability, Xeth offers an alternative interpretation: perhaps women are biologically inferior to men in math ability, but modern cultural factors are producing the external appearance of women having equal math ability.

“But men have greater variance!”

In a comment titled “Simply doesn’t address the real issue”, modded up with “Score:4, Interesting”, an Anonymous Coward writes:

This is an extremely dishonest story which does not address the most basic issues involved. What Summers said at Harvard is supported by the evidence and remains the best explanation for the “gender gap.” Indeed, he felt confident that he could “get away” with his statements because the evidence is so overwhelming and the facts so obvious.

[...]

for virtually all traits the STANDARD DEVIATION of the male distributions is somewhat larger than the female distribution — although not by much. Again this makes some intuitive sense — men are biological more expendable then women so more variation in male traits can be tolerated.

I can hardly be expected to believe that physical traits (the measurement of which is generally not controversial) are unique in having property (4). Especially when the observable data available for mental traits exhibits a difference in standard deviation.

This difference in standard deviation predicts what we see in practice — if we set a high threshold and look at the number of men and women with ability above that threshold we expect the ratio of men to women to be large. Because this is an effect of differences in standard deviation, it is not observable near the middle of the distribution — only at the tails.

There are many many articles which conclude that there is no gender gap in mathematical ability because the mean of the male and female distributions are the same or similar. I am not familiar with every such article, but every one I have read — including the two famous Science articles — presents observational data showing a difference in STANDARD DEVIATION. An issue none of them seem to address.

Once again, the commenter didn’t bother to RTFA, which has already addressed and debunked the “greater male variability hypothesis”. He is already convinced that the study is flawed, that he is more clever than the female researchers, and that women are biologically incapable of excelling in math.

“Females can learn math, but they have difficulty applying it, because their brains are not equipped to do so.”

In a comment titled “The article is confused”, modded up with “Score:5, Interesting”, j. andrew rogers writes:

The article is confused about where most of the real differences are purported to be.

No one credible claims that females have less ability to learn mathematics or crunch numbers in most cases, which is what this article is contesting. In other words, they built themselves a strawman. The differences involve application, not learning.

What *is* credibly claimed, in the sense that there is not insignificant quantities of direct and indirect evidence in literature, is that females are markedly poorer at certain classes of applied mathematical problems, notably applications involving complex, high-dimensionality metric spaces. Females understand the mathematics just fine, they have relative difficulty applying it to real-world problems when system complexity exceeds a certain threshold. This is largely attributed to male brains having more neurons dedicated to conceptualizing and manipulating spatial relationships.

There are real differences, but it is mostly in specific areas of the applied side and there is a relatively straightforward causal theory related to brain structure. That people feel it necessary to repeatedly trot out the strawman that women have less ability to learn math while conveniently ignoring supportable arguments for differences in practical ability reeks of a political agenda. There are other biases in application spaces strongly favoring females that also have straightforward causal links related to differences in brain structure but which say nothing about the ability of males to learn.

There is evidence of a gender difference in ability to manipulate spatial relationships, but there is no consensus that this difference is innate. Some of the indirect evidence used to support this hypothesis is a gender difference in math performance of school-age children and the scarcity of women in math, science, technology, and engineering—which is what this very study addresses.

As for math application and complexity, the paper notes (p. 8802):

Thus, girls have now reached parity with boys in mathematics performance in the U.S., even in high school where a gap existed in earlier decades.

However, coding of the test items on these examinations for cognitive level indicated that none of them tapped complex problem solving at most grade levels for most states (13). Thus, it was impossible with these NCLB data sets to investigate whether a gender gap existed in complex problem solving. Therefore, the researchers also examined data from the National Assessment of Educational Progress (NAEP), a federally managed program that tests a random sample of U.S. students each year (14). Items from 12th grade data categorized by NAEP as hard and by the researchers as requiring complex problem solving were analyzed for gender differences; effect sizes were found to average d = 0.07, a trivial difference. These findings provide further evidence that U.S. girls have now reached parity with boys, even in high school, and even for measures requiring complex problem solving.

“Women are mentally differently-abled. The Politically Correct regime denies innate gender difference even in the face of hard evidence.”

In a comment titled “There is and always will be differences.”, modded up with “Score:5, Insightful”, JustNiz writes:

Whether you like to accept it or not, women and men are psychobiologically different. Meaning, there are observable, quantifiable and consistent physical differences in the brain and its chemistry based solely on gender.

As a result, women consistently perform worse at spatial-based tasks than men. Women consistently perform better at communications-based tasks than men. There are millions of well-conducted experiments and studies that re-prove the existence of these and other gender-based differences over and over again.

It frustrates the hell out of me that the loony ‘Politically Correct’ regime is so enforced on us and continues to reduce to denial any innate gender difference even in the face of hard evidence.

Most ‘normal’ people now feel they can’t even openly raise the possibility, much less the FACT that we actually are mentally differently-abled BECAUSE of gender.

Society as a whole will not properly develop until we accept the existence of gender-based ability differences, including mental, as a fact and move on.

Ironically, this commenter accuses the ‘Politically Correct’ regime of denying innate gender difference in the face of hard evidence, while denying in the face of hard evidence. If he doesn’t like the results, he concludes that the study is biased.

“Women do well in math because they work harder. Men do well because of innate ability. Estrogen and testosterone have different effects on the brain and body.”

In a comment modded up with “Score:5, Insightful”, CorporateSuit writes:

Chemically, testosterone and estrogen have different, powerful effects on the brain and body. Be careful not to call people “bigots” because they celebrate this diversity and seek out the advantages it contains, or you must call yourself a bigot for your intolerance toward anyone who thinks that any notable differences are an evil that needs to be squashed. Yes, with extra effort, one sex can almost always measurably outperform the opposite sex where the opposite sex is more fitted, biologically, to a purpose — but that doesn’t reinforce your point; it contradicts it. If a woman and a man can perform equally at math, but the woman has to study n% longer, then the man is inherently better at math. That’s what inherency means. It’s not politically correct, but it’s nature… however, I vehemently agree that the product of nurture and identity should always have the /choice/ to agree with nature or to struggle to see if it can obtain something better. If a woman chooses to study n% longer than the man to perform equally at math, her identity shows that she would be the better mathematician — because she’s more willing to put forth the necessary effort– but don’t hate or belittle people because they accept what nature has given us as a gift, rather than viewing it as a curse.

The commenter believes that if women do as well as men in math, it must be because they work harder, since they cannot possibly have innate math ability. Of course, he has no evidence for this, except his prior conviction that women are biologically weaker in math, his rationalization being that women have higher levels of estrogen.

Conclusion & How It Works

Sexism, not learning, is the general male geek reaction to the study. Apparently, the mere suggestion that men are not innately superior is a vicious attack on men that must be responded to with hysteria, defensiveness, and mental gymnastics.

Once again, men use sexism to argue that sexism no longer exists. For most male geeks, it is unfathomable that there could be sexism or hostility towards women among male geeks. Sexism is such a constant in male geek culture that it is invisible to them.

(How it Works comic by XKCD. (Randall Munroe knows that sexism is a problem.))

• Janet S. Hyde and Janet E. Mertz. Gender, culture, and mathematics performance. PNAS 2009 106: 8801-8807.
• Culture, Not Biology, Underpins Math Gender Gap at ScienceDaily
• Sharon Begley: The Math Gender Gap Explained at Newsweek
• So Summers was simply wrong. by jj at Feminist Philosophers
• Is There Anything Good About Men? by Roy F. Baumeister (2007) – sexist

One of the major purposes of organizations is to relate and coordinate individuals and groups separated by task and space. The authority structure helps accomplish this by defining, at least partially, who can tell whom to do what, and who has the authority to make what kinds of decisions and to take what actions. This authority structure is supplemented with a structure of explicit and implicit policies, procedures, methods, and rules, which channel and direct many decisions and actions.

A policy is a statement of intent that is made to guide others in their decision making without being so specific as to specify decisions. Theoratically, the top executives of any company, but especially the larger ones, necessarily determine policies that help guide the behavior of people within the organization. However, in fact, people at lower levels often have an important hand in fashioning policy. This happens in two ways. First, people at lower levels make recommendations to those at upper levels. Second, people in upper levels sometimes formalize policies to fit behavior patterns that have already emerged at lower levels. In the latter case, policy follows practice.

A frequent characteristic of policy statements is that they are vague enough to permit managers to select among specific decesions, depending upon the managers’ view of the specific conditions surrounding the decision.

In addition to policies, certain procedures and methods are usually designed to facilitate work. For example, there may be eight discrete steps in a particular work process, and a sequence established for each step. Step three might involve notifying two departments that the first two steps are completed. Such a suggested process is called a procedure. It tells people when they should do something. How they do it is the method they use. The method is formally prescribed in some cases and is left to the operant’s discretion in others. Anyone who fails to follow the prescribed procedures and methods is usually open to censure if problems result. Yet much of life in organizations involves evading required procedures and methods, or redesigning them, and again the reasons are usually people-problems rather than errors in the logic of the design of the procedures and methods.

Most organizations have rules and regulations to supplement policies, procedures, and methods. Rules and regulations say what one must do or not do and often specify penalties for infractions. “No one is to punch another’s third card” is an example. There are no ifs, ands, or buts about it. It says “no one,” period.

So there is a sliding scale from guides (policies) to suggestions (procedures) to requirements (rules and regulations). Nearly all organizations include the entire svcale, but different companies may vary widely in their relative emphasis upon various parts of the scale. At the less specific end of the scale, there is more freedom but less certainty, and the reverse is true of the more specific end. Knowing where a particular organization stands on the scale is thus important in understanding how it functions.

Furthermore, there is wide variability between organizational units (eg., research division versus accounting department) in the reliance placed upon or the attention paid to the policy structure.

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Electropalatometry is a useful clinical and research tool for measuring linguapalatal contact. The goal of this study was to examine intra-speaker variability in performance. Twenty individuals spoke VCV nonsense words using a schwa in the initial position, the 15 palatal consonants, and three corner vowels, //, /i/, /u/. A variability index was created to examine speaker consistency.

Different aspects of articulation (i.e. place, manner, voicing, coarticulation) were compared. Significant differences for variability were found for place of articulation in the /i/ vowel context and for manner of articulation in the // vowel context. Also for the // vowel, significant differences were found between the commonly misarticulated /l/, /r/, and /s/. The data reveal differences in production variability across sounds within an individual, as well as from one speaker to another. This knowledge of typical performance may guide the interpretation of data from disordered speakers in future studies.

from the Journal of Communication Disorders