Feminine feet: a study in contrasts

Two current exhibits at the New York Historical Society offer a study in contrasts in representing the feminine ideal as represented by their feet. In one feet are said to become worthy of their own cameras on the red carpet when they are encased in shoes like the diamond encrusted sandals pictured below:

$1,090,000 dollar sandals  decorated with 464  Kwiat diamonds.  In 2002, these diamond shoes were worn by Oscar nominee Laura Harring. Supposedly, that's what started the trend of a cameras placed to capture footwear at the Oscars. A replica of these shoes are  the first object in the current exhibit, Walk This Way: Footwear from the Stuart Weitzman Collection of Historic Shoes.

A number of problems I had with the exhibit:

1. The shoes are arranged in a particularly logical order like a chronological one or even an arrangement of shoes for work and shoes for parties and shoes for occasions. The arrangement keeps jumping around in history. There is some attempt to tie some shoes to historical events-- from changes in hemlines and dance styles to women's role in producing shoes, but there is no particularly cohesive story line.

2. The exhibit is about 90%  decorative but impractical high heels.  You can hear Weitzman talking about the eternal quality of high heels in the exhibit's audio tour. He claims they will always be around because nothing makes legs look better. He seems oblivious to the fact that not all women are willing to sacrifice their comfort and stability to heighten their decorative appeal. He also seems to be unaware of the trend since the 80s (as far as I recall from my own exposure to shoe brands and ads) has been to offer women shoe options that actually allow them to walk beyond a red carpet. Even before that, there were always women whose first priority for shoes had to be something durable.

This brings me to the point of contrast on what we idealize in women I noticed when viewing Norman Rockwell's famous "Rosie the Riveter" painting in full in the Rockwell, Roosevelt & the Four Freedoms exhibit.  One freedom that is not included in the four is the freedom of movement for women constrained by feminine fashion. Here we see a heroine for the World War II period named Rosie who is dressed in practical clothes with practical shoes (no high heels on a job that requires you to be stable on your feet). In fact, her comfortable loafers are poised over a copy of Mein Kampf. It is not delicate footwear that will defeat tyranny and hate but sensible shoes worn by a woman who is willing to get her face dirty and get the job done. 

Rosie is, in fact, shown as angel with a dirty face. As the description of the painting points out, her protective mask is pushed up in a position to assume the shape of a halo.  She is depicted as the strong female force of good that will stamp of evil. 

The exhibit also shows the usual poster associated with Rosie the Riveter, which is not a full length picture and includes the slogan "We can do it!" This version, which you can see in the still from the video below, was not created by Normal Rockwell but the Pittsburgh artist J. Howard Miller. Why more than one Rosie? The idea of Rosie the Riveter was depicted in song. You can hear it on this video. 

If you'd like to see a video that offers more information on the Four Freedoms and Rockwell's depiction of them, you can click on this video.

Sex, Lies, and Data Profiles

The title of this blog could have been the title for Seth Stephens-Davidowitz's book Everybody Lies. As he explained in his interview with Freankonomics' Stephen Dubner, he knew the title is inaccurate, though he was told that "98% of people lie" wouldn't sell well. In case you were wonderings about the Cretan or Liar's Paradox implied by the title he opted for, he assures the interviewer that he is among the 2% of honest people. But didn't he then lie in the title? And is he really as honest as he claims? This blog examines the second question.

Ultimately, what makes both data reports from people who are presented as experts and data visualization so effective at conveying a point is that they don’t require much analysis on the viewer’s end because they’ve already done the thinking for you. That’s both seductive and potentially misleading.

That’s exactly why we have to be careful about not merely accepting the visually expressed story at face value. Any data visualization should be subjected to a triple C test with a check for context, correlation, and causation that I wrote about for Baseline  here.

In light of how openly political media and companies that handle data have grown, there's clearly a need for a few more C words to keep in mind when presented what is offered as objective data:

• Correspondence to reality. Just because someone claims expertise doesn't mean they are completely correct about their assertions. For example, when I was in labor with my first baby, the doctors and nurses at the hospital just dismissed my pains, claiming the contractions were "mild" and that the birth was far from imminent. I was not the expert; they were, but I knew that I felt the baby coming. As it turned out, the resident barely got to me in time. I learned from that experience that you should not be gaslighted by expert views that directly contradict not what you just think you know but what you do know and directly experience. 
• Convenience: This pertains to both means and ends. Convenience of means refers to using the data that is on hand or easily measured even if it's not necessarily the data that is the most relevant. It's rather like measuring how much snow fell on your windowsill because it's easy to reach rather than going out to get the measure on the street and in drifts to get a more accurate measurement. Convenience for ends is about selecting data that you can easily fit into the conclusion you wish to draw AKA cherry picking. 
• Confirmation Bias:In general, when you look for data on something, you have to bear in mind that absolute objectivity is rare. Many of us have deeply-seated values and beliefs that will not allow us to entertain the possibility that we are on the wrong track,which would skew our results because of what we allow and disallow in the data set. It is the equivalent to painting a bull's eye around where your arrow went. So ask yourself, does the person have some personal agenda that could be coloring the outcome? If so you should treat them with the same healthy skepticism you would treat cigarette tobacco studies sponsored by tobacco companies. 
• Certainty Camouflaging Contingencies: Few things are absolutes, so if someone states something without qualifiers, likely something is being hidden or glossed over -- like the fact that the data is out of date or taking searches of racist terms and jokes as proxies for the person being a racist and then shifting labels from what actually is measured to what the person says is signified by the measurement. This leads to a triple F: Fudging Figures and Facts.

Incidentally, Seth Stephens-Davidowitz takes no chances that you won't recognize him as an expert. Right on p. 1, he declares, "I am an internet data expert." I don't make any such claim, though I have been delving into question of big data since 2011 and regularly review data science student work. But unlike Stephens-Davidowitz, I didn't work for Google. It was actually a team from Google that originally inspired me to write up the piece on not believing everything you see in data visualizations.

The data visualization a the beginning of Everyone Lies( p.13) presents two maps of the US that intends to show a correlation that implies causation. Stephens-Davidowitz refers to having researched correlations of racist searches with voter patterns to argue that Obama lost votes to racism. However the argument he makes about Trump right at the beginning of his book is actually based on an assertion that Nate Silver made in a tweet in early 2016.

Nate Silver's tweet cites an article written by a different Nate with the last name Cohen to bolster his claim. So I went to his source: a New York Times article written by  published on December 31, 2015, Donald Trump’s Strongest Supporters: A Certain Kind of Democrat, and there are the maps  that appears in Stephens-Davidowitz's book.

The maps that are juxtaposed to indicate correlation and imply causation in the article that reappear (in grayscale) in Everybody Lies. In case the caption appears too small for you to read, I'll put it in text: "Source: Vote estimates by Congressional district provided by Civis Analytics; Google search estimates from 2004-7 by Seth Stephens-Davidowitz. How convenient! Stephens-Davidowitz already had that data set from when he gathered it to present evidence of racism at the time of Obama's election. So what if it was really past its sell by date in 2015, never mind in 2017, recycling is a good thing, isn't it?

Aside from the lack of color, there are two other differences in the maps that appear in the book: One it doesn't have the identifier by year. Two: the more cautious label applied in the newspaper illustration of "Where racially charged Internet searches are most common" is replaced by the more confidently asserted "Racist Search Rate." If you suspect that they are, in fact, different maps, I can only tell you to open the book and look for yourself to be assured that I am not misrepresenting anything. This is an example certainty camouflaging contingencies.

Here's my simple Venn diagram of an assertion Stephens-Davidowitz made in asserting in the live presentation, as he did in his book, that the biggest single predictor of a vote for Trump was being a racist. The two circles overlap almost completely.

My own illustration of Seth Stephens-Davidowitz's contention

But if you start looking at the data he used to justify this conclusion, you see it's not at all this simple.


It's true that Nate Cohen is hoping to insinuate the Trump support includes areas that tend to more racist, though he is smart enough to qualify the argument: "That Mr. Trump’s support is strong in similar areas does not prove that most or even many of his supporters are motivated by racial animus. But it is consistent with the possibility that at least some are. "


The article also reflects understanding that things are really not so black and white in Democrat vs. Republican presidential elections: "Many Democrats may now even identify as Republicans, or as independents who lean Republican, when asked by pollsters."


Remember the NY Times' article title? That's the main argument, not really the twist that Silver gave it, as many replies to his tweet pointed out: "Mr. Trump appears to hold his greatest strength among people like these — registered Democrats who identify as Republican leaners — with 43 percent of their support, according to the Civis data"

While the article merely suggests that racial attitude could be involved, Stephens-Davidowitz goes even further than Nate Silver's tweet, asserting that racism is the strongest indicator of a Trump vote. That is what he said in the live presentation I heard on April 19th. At the end of the event, I went up to him and asked how is it possible to link the person who searched for things like racist jokes with votes for Trump.

He admitted that would be impossible. Instead, he said, they look at the areas where Trump won and correlate that with areas where there have been searches he identifies as racist to draw this conclusion that racism was the definitive motivating factor in votes for Trump.

 He indicated that the correlations were made based on the verified fact of which states voted for Trump correlated with the type of Google searches that, he contends, identifies a person as racist, and that was conclusive enough for him.

What he failed to admit was that the correlations were not made on the basis of actual voting results but on earlier maps of projected Trump support.

 A look at the actual map of the election results shows a different story. The predictions included just a fraction of the states that did go to Trump, which means they failed to represent the voters overall, a serious failing in what is presented as comprehensive and accurate data.


Let's take a closer look. The maps paired by Stephens-Davidowitz imply a correlation between his findings of data searches that ended in 2007 with the Trump support assumed to be in place in 2015.So remember all the steps of remove we have here:

1. 1.We have search data for racial jokes, the n-word, and the like, on which basis we are to assume that all (or at least most) voters of a particular state can be characterized as racist overall if the percentages of such searches are higher than average.
2. 2. Furthermore, we must assume that in the course of nearly a decade, all the racists stayed in place and retained their views.While such an assumption of stasis may have worked a hundred years ago, it is somewhat doubtful that it can hold in the 21st century when things move at broadband speed.
3. 3. We have to consider that the overlap of higher support for Trump and higher racism rankings are not just a correlation but an indication of a causal relationship, as he explicitly identified it as an accurate predictor.

The whole theory could possibly be woven together to appeal to those who already favor that outcome, but it doesn't hold water. Even if I'd grant Stephens-Davidowitz that most of the people who had conducted those internet search about a decade before the election stayed in place and did cast their vote for Trump, the visualization would look like this:














Of course, that is not wholly accurate either, as we don't have a clearly established relationship between the people who made racially charged searches back when their search data was collected and the voting citizens of the area in 2016. But this represents the fact that even if some racists are including in the voting pool for Trump, it doesn't define all the voters in that pool. As we can see from the maps of actual voting results with Stephens-Davidowitz's own map of racism, Trump voters were not confined to those states. See the comparison shown by the juxtaposition below.

This reveals that the correlation that Stephens-Davidowitz's points to is not nearly as causative as implied. First of all, some of the places he identified as leaning toward racism in the west actually voted for Clinton. Second of all, the states that did vote for Trump far exceed the ones identified as inclined toward racism. So inclusion of some states with racist searches in the Trump wins is not a definitive correlation because it only includes a portion (not a definitive majority) and fails to account for the voters overall.


Ultimately, what Stephens-Davidowitz's set of maps really shows is not conclusive proof that racism is the best predictor of votes. Instead, what we have in the argument is an illustration of of a confirmation bias that clings to outdated, misleading, and factually wrong representation even when we have access to data that disproves the theory. 

While maps of actual votes were available by the time he published his book,  he kept the map of incorrect predictions as the definitive map of Trump votes because it fits the hypothesis better. It just didn't fit the reality because with that limited support, he would not have won the election.

Sticking with old data sets because they are convenient -- both in terms of saving you research time and in terms of fitting what you want to prove -- is not true data science as it runs contrary to the essential value of science. Richard Feynman touched on this issue in a 1974 address to Caltech entitled Cargo Cult Science in which he explained that true science is about doing one's best "to give all of the information to help others to judge the value of your contribution; not just the information that leads to judgment in one particular direction or another."

This is not to say that we must put Trump on a pedestal.No matter whether you love the president,hate him, or like many others, are somewhat neutral and willing to judge based on results, you should still not distort data to support a particular narrative.

At this point, you may be thinking, "Well that's all politics, but what about the sex in the title?" It's there because Stephens-Davidowitz uses examples related to that to try to capture attention, as demonstrated by what he starts with in his book, his interview (cited above) and the live presentation I heard.

I didn't take on the deconstruction of that, though someone else did. See the second part of Chelsea Troy's review, Everybody Lies’ Review Part 2: Dangerous Methodology She brings up the issue of bad proxies and misleading numbers. I couldn't agree more with what she says here: "Just because there are some numbers floating around doesn’t make a study valid."