• # Dictionary of Numbers extension adds context to numbers

July 8, 2013  |  Statistics

We read and hear numbers in the news all the time, but it can be hard to imagine what those numbers mean. For example, big numbers, on the scale of billions, are hard to picture in our head, because we don't typically handle that many things at one time. Most of us have never seen a billion dollars plopped in front of us. The Dictionary of Numbers, a Google Chrome extension by Glen Chiacchieri, can help you out in this department.

I noticed that my friends who were good at math generally rely on "landmark quantities", quantities they know by heart because they relate to them in human terms. They know, for example, that there are about 315 million people in the United States and that the most damaging Atlantic hurricanes cost anywhere from \$20 billion to \$100 billion. When they explain things to me, they use these numbers to give me a better sense of context about the subject, turning abstract numbers into something more concrete.

When I realized they were doing this, I thought this process could be automated, that perhaps through contextual descriptions people could become more familiar with quantities and begin evaluating and reasoning about them.

Install the extension, and as shown in the video above, it injects inline descriptions next to numbers in articles. You can also use the search box. Enter "100 meters" and you get "about the height of the Statue of Liberty." Although still rough around the edges (It seems to find descriptions for a limited index of numbers.), the Dictionary is an interesting experiment in making numbers for relatable.

• # Statistics jokes

June 27, 2013  |  Statistics

There's a fun CrossValidated thread on statistics jokes. Here's the one with the top votes:

A statistician's wife had twins. He was delighted. He rang the minister who was also delighted. "Bring them to church on Sunday and we'll baptize them," said the minister. "No," replied the statistician. "Baptize one. We'll keep the other as a control.

This line by George Burns is my favorite though:

If you live to be one hundred, you've got it made. Very few people die past that age.

Any other good ones?

• # Beer recommendation system in R

June 21, 2013  |  Statistics

Using data from Beer Advocate, in the form of 1.5 million reviews, yhat shows how to build a recommendation system in R.

The goal for our system will be for a user to provide us with a beer that they know and love, and for us to recommend a new beer which they might like. To accomplish this, we're going to use collaborative filtering. We're going to compare 2 beers by ratings submitted by their common reviewers. Then, when one user writes similar reviews for two beers, we'll then consider those two beers to be more similar to one another.

The simple recommender is at the end of the article. Select a beer you like, a type of beer you want to try, and you get a handful of beers you might like.

Obviously, the method isn't exclusive to beer reviews, and this is just a start to a more advanced system that you can tailor to your own data. The good news is that the code to scrape data and recommend things is there for your disposal. [via @drewconway]

• # Twitter trend detection algorithm

June 19, 2013  |  Statistics

Stuff happens, and people tweet about it. Something major happens, and a lot of people tweet about it. Masters student Stanislav Nikolov and his adviser Devavrat Shah are working on ways to algorithmically detect the latter.

People acting in social networks are reasonably predictable. If many of your friends talk about something, it's likely that you will as well. If many of your friends are friends with person X, it is likely that you are friends with them too. Because the underlying system has, in this sense, low complexity, we should expect that the measurements from that system are also of low complexity. As a result, there should only be a few types of patterns that precede a topic becoming trending. One type of pattern could be "gradual rise"; another could be "small jump, then a big jump"; yet another could be "a jump, then a gradual rise", and so on. But you'll never get a sawtooth pattern, a pattern with downward jumps, or any other crazy pattern.

And with that, the algorithm compares current patterns to the ones above. If they look like a trending pattern, the algorithm marks something as a trend with some probability. In testing with past trending topics, the algorithm was able to pick correctly over 90 percent of the time.

The best part is that this method can be applied to other time series data. "We can try this on traffic data to predict the duration of a bus ride, on movie ticket sales, on stock prices, or any other time-varying measurements."

• # Non-statistician analysts are the new norm

June 17, 2013  |  Statistics

As data grows cheaper and more easily accessible, the people who analyze it aren't always statisticians. They're likely to not even have had any statistical training. Biostatistics professor Jeff Leek says we need to adapt to this broader audience.

What does this mean for statistics as a discipline? Well it is great news in that we have a lot more people to train. It also really drives home the importance of statistical literacy. But it also means we need to adapt our thinking about what it means to teach and perform statistics. We need to focus increasingly on interpretation and critique and away from formulas and memorization (think English composition versus grammar). We also need to realize that the most impactful statistical methods will not be used by statisticians, which means we need more fool proofing, more time automating, and more time creating software. The potential payout is huge for realizing that the tide has turned and most people who analyze data aren't statisticians.

Yep.

Those who disagree tend to worry what might happen — what kind of data-based decisions will be made — by non-statisticians, and that should definitely be a priority as we move forward. Non-statisticians often make incorrect assumptions about the data, forget about uncertainty, and don't know much about collection methodologies.

However, as a statistician (or someone who knows statistics), you can shoo everyone else away from the data and gripe when they come back, or you can help them get things right.

• # The differences between a geek and a nerd

June 14, 2013  |  Statistics

Curious about how people use "geek" and "nerd" to describe themselves and if there was any difference between the two terms, Burr Settles analyzed words used in tweets that contained the two. Settles used pointwise mutual information (PMI), which essentially provided a measure of the geekness or nerdiness of a term. The plot above shows the results.

In broad strokes, it seems to me that geeky words are more about stuff (e.g., “#stuff”), while nerdy words are more about ideas (e.g., “hypothesis”). Geeks are fans, and fans collect stuff; nerds are practitioners, and practitioners play with ideas. Of course, geeks can collect ideas and nerds play with stuff, too. Plus, they aren’t two distinct personalities as much as different aspects of personality. Generally, the data seem to affirm my thinking.

Or maybe pop culture (geek) versus education (nerd).

• # Hans Rosling explains population growth and climate change

June 7, 2013  |  Statistics

Because every day is a good day to listen to Hans Rosling talk numbers. In this short video, Rosling uses Lego bricks to explain population growth and the gaps in wealth and carbon footprint.

• # Myths of big data

June 4, 2013  |  Statistics

Microsoft researcher Kate Crawford describes several myths of big data. Myth #4: It makes cities smarter.

"It's only as good as the people using it," Ms. Crawford said. Many of the sensors that track people as they manage their urban lives come from high-end smartphones, or cars with the latest GPS systems. "Devices are becoming the proxies for public needs," she said, "but there won't be a moment where everyone has access to the same technology." In addition, moving cities toward digital initiatives like predictive policing, or creating systems where people are seen, whether they like it or not, can promote lots of tension between individuals and their governments.

Yep. I hear those people things can introduce a lot of challenges.

• # Medicare provider charge data released

May 28, 2013  |  Data Sources

The Centers for Medicare and Medicaid Services released billing data for more than 3,000 U.S. hospitals, showing high variance in cost of health scare across the country and even between nearby hospitals.

As part of the Obama administration’s work to make our health care system more affordable and accountable, data are being released that show significant variation across the country and within communities in what hospitals charge for common inpatient services.

The data provided here include hospital-specific charges for the more than 3,000 U.S. hospitals that receive Medicare Inpatient Prospective Payment System (IPPS) payments for the top 100 most frequently billed discharges, paid under Medicare based on a rate per discharge using the Medicare Severity Diagnosis Related Group (MS-DRG) for Fiscal Year (FY) 2011. These DRGs represent almost 7 million discharges or 60 percent of total Medicare IPPS discharges.

The data is downloadable as CSV or Excel files and is surprisingly usable and worth a look.

The New York Times has a useful per-hospital browser and The Washington Post provides quick comparisons by state.

• # Convergence of Miss Korea faces

May 20, 2013  |  Statistics

After seeing a Reddit post on the convergence of Miss Korea faces, supposedly due to high rates of plastic surgery, graduate student Jia-Bin Huang analyzed the faces of 20 contestants. Below is a short video of each face slowly transitioning to the other.

From the video and pictures it's pretty clear that the photos look similar, but Huang took it a step further with a handful of computer vision techniques to quantify the likeness between faces. And again, the analysis shows similarity between the photos, so the gut reaction is that the contestants are nearly identical.

However, you have to assume that the pictures are accurate representations of the contestants, which doesn't seem to pan out at all. It's amazing what some makeup, hair, and photoshop can do.

You gotta consider your data source before you make assumptions about what that data represents.

• # Length of the average dissertation

May 8, 2013  |  Statistics

On R is My Friend, as a way to procrastinate on his own dissertation, beckmw took a look at dissertation length via the digital archives at the University of Minnesota.

I've selected the top fifty majors with the highest number of dissertations and created boxplots to show relative distributions. Not many differences are observed among the majors, although some exceptions are apparent. Economics, mathematics, and biostatistics had the lowest median page lengths, whereas anthropology, history, and political science had the highest median page lengths. This distinction makes sense given the nature of the disciplines.

I was on the long end of the statistics distribution, around 180 pages. Probably because I had a lot of pictures.

As I was working on my dissertation, people often asked me how many pages I had written and how many pages I had left to write. I never had a good answer, because there's no page limit or required page count. It's just whenever you (and your adviser) feel like there's enough to get a point across. Sometimes that takes 50 pages. Other times it takes 200.

So for those who get that dreaded page-count question, you can wave your finger at this chart and tell people you're somewhere in the distribution.

• # The Numbers Game on National Geographic

April 29, 2013  |  Statistics

Jake Porway, the founder of DataKind, has a new show on the National Geographic channel called The Numbers Game. I unfortunately don't have the channel, so the clips on the site will have to suffice for now.

Keep in mind this show is for a wide audience though. Jake notes:

Now for those of you who have been writing to me excited that Big Data is finally getting its own TV show, I should point out that this show is a lot more like a science show than a show about data. You won’t find discussions about Hadoop, machine learning, or even the basics of correlation vs. causation here. Instead, the show tries to make the latest statistics accessible to a wide audience of people who may just be dipping their toes in to this new world of data. It’s more Guy Fieri than Carl Sagan, but it’s a blast.

The first of three episodes aired last week, and the second is on tonight. You should watch it.

• # Flexible data

April 17, 2013  |  Statistics

Data is an abstraction of something that happened in the real world. How people move. How they spend money. How a computer works. The tendency is to approach data and by default, visualization, as rigid facts stripped of joy, humor, conflict, and sadness — because that makes analysis easier. Visualization is easier when you can strip the data down to unwavering fact and then reduce the process to a set of unwavering rules.

The world is complex though. There are exceptions, limitations, and interactions that aren't expressed explicitly through data. So we make inferences with uncertainty attached. We make an educated guess and then compare to the actual thing or stuff that was measured to see if the data and our findings make sense.

Data isn't rigid so neither is visualization.

Are there rules? There are, just like there are in statistics. And you should learn them.

However, in statistics, you eventually learn that there's more to analysis than hypothesis tests and normal distributions, and in visualization you eventually learn that there's more to the process than efficient graphical perception and avoidance of all things round. Design matters, no doubt, but your understanding of the data matters much more.

• # Problematic databases used to track employee theft

April 3, 2013  |  Data Sharing

Employee theft accounts for billions of dollars of lost merchandise per year, so it's a huge concern for retailers, but it often goes unreported as a crime. If only there were reference databases where business owners could report offenders and look up potential employees to see if they have ever stole anything. It turns out there are, but the systems have proved to be problematic.

"We're not talking about a criminal record, which either is there or is not there — it's an admission statement which is being provided by an employer," said Irv Ackelsberg, a lawyer at Langer, Grogan & Diver who represents Ms. Goode.

Such statements may contain no outright admission of guilt, like one submitted after Kyra Moore, then a CVS employee, was accused of stealing: "picked up socks left them at the checkout and never came back to buy them," it read. When Ms. Moore later applied for a job at Rite Aid, she was deemed "noncompetitive." She is suing Esteem.

On paper, the data sounds great for business owners, and keeping such data also seems like a fine business to run. Thefts go down and owners can focus on other aspects of their business. The challenge and complexity comes when we remember that people are involved.

• # How to become a password cracker in a day

March 26, 2013  |  Statistics

Deputy editor at Ars Technica Nate Anderson was curious if he could learn to crack passwords in a day. Although there's definitely a difference between advanced and beginner crackers, openly available software and resources make it easy to get started and do some damage.

After my day-long experiment, I remain unsettled. Password cracking is simply too easy, the tools too sophisticated, the CPUs and GPUs too powerful for me to believe that my own basic attempts at beefing up my passwords are a long-term solution. I've resisted password managers in the past over concerns about storing data in the cloud or about the hassle of syncing with other computers or about accessing passwords from a mobile device or because dropping \$50 bucks never felt quite worth it—hacks only happen to other people, right?

But until other forms of authentication take root, the humble password will form a primary defense of our personal information. The time has come for me to find a better solution to generating, storing, and handling them.

• # Odds of a perfect NCAA March Madness bracket

March 22, 2013  |  Statistics

Math professor Jeff Bergen explains the odds of picking a perfect bracket.

The first probability is based on a 50/50 split of correct picks, which is like using fair coin flips to pick winners. Bergen doesn't really go into how he calculated the second probability, but that smaller number comes up by bumping up the probability of picking the right team for each game. I think he's using an average probability of slightly less than 70% (based on simulation results from this old Wall Street Journal column).

That's why businesses can offer up million dollar prizes. In all likelihood, no one is going to win, which turns out to be a great business model for insurance companies who back these contests:

If millions of people enter a particular contest, it might seem like the chance of someone winning is suddenly in the realm of possibility. But there's a catch: This scenario assumes everyone maximized their chances by picking mostly favorites, so those with the best shot at winning are likely to have identical entries. These contests generally protect themselves from big losses by stating they'll divvy up the loot if there are multiple perfect brackets.

These favorable conditions make insuring these prize offers a good business, as the Dallas company SCA Promotions has discovered. SCA, founded by 11-time world bridge champion Robert D. Hamman, has taken on the insurance risk for roughly 50 perfect-bracket prizes -- including a Sporting News offer of \$1 million in 2001, according to vice president Chris Hamman, the founder's son. In the 12 years it has been doing so, SCA has never had to pay out a claim.

• # Declining songwriter ratings with age

March 21, 2013  |  Statistics

Do singer-songwriters age well like a fine wine, or does quality decline with age? Kyle Biehle analyzed fan ratings by age.

I understand all of the reasons for not comparing artists in this way. Despite twenty-one Academy Award nominations, Woody Allen never attends the Oscars. His reason is that art isn't competition — judging art is so subjective who's to say who or what is best? After all one man's Poison is another man's Cream. Similarly, Elvis Costello (featured in the viz) is famously credited with saying: "Writing about music is like dancing about architecture - It's a really stupid thing to want to do." I agree that using ratings - whether from fans or critics — to judge artistic merit is at best flawed and at worst a fool's exercise.

But I wanted to do it anyway.

Most peak in their 20s and either stabilize later on or continue to decline. Occasionally, as in the case with Bob Dylan, there's some see-sawing. Take a look at the Tableau interactive for a closer look. [via Waxy]

• # Data hackathon challenges and why questions are important

March 12, 2013  |  Statistics

Jake Porway, executive director of DataKind on data hackathons and why they require careful planning to actually work:

Any data scientist worth their salary will tell you that you should start with a question, NOT the data. Unfortunately, data hackathons often lack clear problem definitions. Most companies think that if you can just get hackers, pizza, and data together in a room, magic will happen. This is the same as if Habitat for Humanity gathered its volunteers around a pile of wood and said, "Have at it!" By the end of the day you'd be left with a half of a sunroom with 14 outlets in it.

Without subject matter experts available to articulate problems in advance, you get results like those from the Reinvent Green Hackathon. Reinvent Green was a city initiative in NYC aimed at having technologists improve sustainability in New York. Winners of this hackathon included an app to help cyclists "bikepool" together and a farmer's market inventory app. These apps are great on their own, but they don't solve the city's sustainability problems. They solve the participants' problems because as a young affluent hacker, my problem isn't improving the city's recycling programs, it's finding kale on Saturdays.

Without clear direction on what to do with the data or questions worth answering, hackathons can end up being a bust from all angles. From the organizer side, you end up with a hodgepodge of projects that vary a lot in quality and purpose. From the participant side, you're left up to your own devices and have to approach the data blind, without a clear starting point. From the judging side, you almost always end up having to pick a winner when there isn't a clear one, because the criteria of the contest was fuzzy to begin with.

This also applies to hiring freelancers for visualization work. You should have a clear goal or story to tell with your data. If you expect the hire to analyze your data and produce a graphic, you better get someone with a statistics background. Otherwise, you end up with a design-heavy piece with little substance.

Basically, the more specific you can be about what you're looking for, the better.

• # What data brokers know about you

March 11, 2013  |  Statistics

Lois Beckett for ProPublica has a thorough piece on data brokers — companies that collect and sell information about you — and what they know and where they get the data from.

They start with the basics, like names, addresses and contact information, and add on demographics, like age, race, occupation and "education level," according to consumer data firm Acxiom's overview of its various categories.

But that's just the beginning: The companies collect lists of people experiencing "life-event triggers" like getting married, buying a home, sending a kid to college — or even getting divorced.

Credit reporting giant Experian has a separate marketing services division, which sells lists of "names of expectant parents and families with newborns" that are "updated weekly."

The companies also collect data about your hobbies and many of the purchases you make. Want to buy a list of people who read romance novels? Epsilon can sell you that, as well as a list of people who donate to international aid charities.

So if you're wondering why you received that catalog in the mail, it was probably because a store sold your purchase data to a broker.

• # Using search data to find drug side effects

March 8, 2013  |  Statistics

Along the same lines as Google Flu Trends, researchers at Microsoft, Stanford and Columbia University are investigating whether search data can be used to find interactions between drugs. They recently found an interaction.

Using automated software tools to examine queries by six million Internet users taken from Web search logs in 2010, the researchers looked for searches relating to an antidepressant, paroxetine, and a cholesterol lowering drug, pravastatin. They were able to find evidence that the combination of the two drugs caused high blood sugar.

The idea is that people are searching for symptoms and medications, and this data is stored in anonymized search logs. They then followed a suspicion that using the two drugs at the same time might cause hyperglycemia. Those that searched for the two drugs were more likely to search for hyperglycemia than the control group (probably those who didn't search for hyperglycemia).

The work is still in its infancy, but it'll be interesting to see how this sort of data can be used to supplement existing work by the Food and Drug Administration.

Unless otherwise noted, graphics and words by me are licensed under Creative Commons BY-NC. Contact original authors for everything else.