Wednesday, December 13, 2017

Harry Potter and the Gloriously Unhinged Story

Via Mashable, Botnik Studios, a creative community, just gave us a new Harry Potter chapter, that was written using a predictive algorithm trained on the seven Harry Potter books:

And it's hilarious. Here are a few excerpt:
"What about Ron magic?" offered Ron. To Harry, Ron was a loud, slow, and soft bird. Harry did not like to think about birds.

The password was "BEEF WOMEN," Hermione cried.

"Voldemort, you're a very bad and mean wizard," Harry savagely said. Hermione nodded encouragingly. The tall Death Eater was wearing a shirt that said, 'Hermione Has Forgotten How To Dance,' so Hermione dipped his face in mud.

The pig of Hufflepuff pulsed like a large bullfrog. Dumbledore smiled at it, and placed his hand on its head: "You are Hagrid now."

Tuesday, December 12, 2017

Roy Moore's Interview

Each day, we're hearing of more men and women coming forward to talk about inappropriate behavior from some of the most powerful men in the country. And while in many cases, those accusations are being treated as serious, in one instance, the reaction is just getting more and more tone-deaf. (Or perhaps I should say "Moore and Moore tone-deaf.")

In a move that I was absolutely certain was satire when I first heard about it, Roy Moore sat down with 12-year-old Millie March for an interview. The interview was arranged by a Pro-Trump group created by former Breitbart staffers. The goal of the move is to show that Moore can be in the same room as a child and not be creepy or assault her, right?

Dear god, where to begin on this one? Sure, Moore is on his best behavior when the cameras are rolling. But the issue brought forward with all of these accusations is a penchant for these powerful men to treat women like objects, to use them as means to an end. Is that any different than what is happening in this interview? Millie isn't being treated as a person; she's a prop. A bargaining chip used to get what Moore and this Pro-Trump group want - for Moore to be elected. Sure, he didn't assault or harass her. But he and everyone else involved in setting up that interview still objectified her.

Thankfully, I'm not the only one who is disgusted by this stunt:
On Twitter and elsewhere, people were quick to point to the uncomfortable decision to use a 12-year-old girl for a campaign push.

Democratic strategist Paul Begala called it “appalling” and “shocking.”

“The fact that he’s accused of sexual assaulting a 14-year-old girl, would sit down and do an interview with a 12-year-old, when he’s not talking to any journalists—it’s like he’s rubbing Alabamians’ noses in it,” he said.
In summation, I leave you with this brilliant tweet by Franchesca Ramsey:

Monday, December 11, 2017

Follow-Up on "Cat Person"

On Saturday, I shared a story published in The New Yorker: Cat Person by Kristen Roupenian. It's an excellent read I highly recommend.

Today, I discovered someone set up a Twitter account that just retweets negative reactions to the story by men. It's glorious.

And yes, before you say it, I know #NotAllMen hated this story. And I would imagine, many of these men who are responding negatively to the story are self-professed nice guys - in my estimation, probably the ones who say idiotic expressions like YOLO and "nice guys finish last" completely in earnest. But if words like "whore" and "cunt" and "bitch" are right on the tip of your tongue when a woman doesn't respond in the way you'd like, sorry, but you're not a nice guy. And if you find yourself rooting for a guy who calls a woman a whore just because she isn't interested in seeing him, I suggest you take a good long look at yourself: you're part of the problem.

Mama Always Told Me Not to Look into the Eyes of the Sun

What happens to your eyes when you look directly at the sun? A woman in her 20s, the solar eclipse, and 6 seconds have helped us get the answer to that question:
By the time the 20-something woman in today’s case study — published in the journal JAMA Ophthalmology — looked at the sun, it was already 70 percent covered by the moon. Three days later, she headed to the Mount Sinai’s New York Eye and Ear Infirmary, where doctors informed her that she had damaged her retinas by looking into a giant ball of glowing gas that emits radiation that burns your eyes.

Images of her eyes are the first time we’ve been able to see such detailed pictures, thanks to advances in optics. These showed that both eyes were affected, with the left eye especially having damaged photoreceptors and a lesion. Unfortunately, no treatment for eye damage from staring at the sun — technically called solar retinopathy — currently exists. 
It only took 6 seconds for her to do permanent and serious damage. Here are the images of her retina published in the article:

Hopefully you got to see the eclipse. And hopefully, you used the proper eye protection so you'll be able to see the next one.

Writing Goals for the New Year

It's only December 11, but I'm already thinking about next year and what I'd like to accomplish. And just as I did last December, many of my goals have to do with writing.

First, I'd like to write more short stories. I used to write them constantly, but now I've been focused more on writing blog posts and, during the month of November at least, novels. But short fiction is a great way to practice and improve, and can lead to ideas for longer fiction. Though I'd love to follow Ray Bradury's recommendation of writing a short story a week, that might be a lofty goal alongside writing regular blog posts and finishing NaNoWriMo novels. I plan on setting a number goal, but it will be less than 52.

Second, I want to participate in more writing contests. I just found this great curated list of writing contests for 2018, which includes many free contests. I had a lot of fun participating in the NYC Midnight short story contest earlier this year, and just registered to do that contest again. And if I'd like to participate in one before 2018, Writer's Digest has one running until this Friday, December 15. We'll see if I have time to sit down and write 1500 words between now and then.

My third goal is to get better about writing down ideas. I always say I'll remember them, and then later I can't. This may mean I'll have to stop in the middle of a conversation with someone to pull out my notebook or get out of bed in the middle of the night to jot something down. I'd love to see if I'm a more productive writer as a result.

Last year, I made the goal to average out to 1 blog post a day. Here I am, close to the end of the year, and I'm scrambling just a bit to make up a deficit. I'm going to try to make it, but I've decided not to set a number goal for blog posts in 2018. More doesn't necessarily equal better. I'll have some blogging goals, and one of those will definitely be to participate in April A to Z again (you can check out 2016 and 2017), though I'm not sure what my theme will be. More specific goals later.

Sunday, December 10, 2017

Statistics Sunday: Bayesian Inference in a Galaxy Far Far Away

I was recently rewatching Rogue One with a friend the other day. Since this is part of the Star Wars universe, it of course had to have some of the usual Star Wars elements: strange-looking aliens, someone uttering the line "I've got a bad feeling about this," and droids rambling off odds of different outcomes. Always bad outcomes - seriously, why don't the droids ever feel the need to say, "The odds are 50 to 1 that everything is going to turn out okay," or "There are puppies ahead; 200 to 1 odds of many puppy snuggles"?

But I digress. Because what I really want to talk about are those odds, and why they tell us something about the droids. True, they're sprinkled into the movies mainly as jokes. We don't really need to pay attention to the odds, other than to be impressed when the bad thing the droid was calculating on about doesn't end up happening. For instance, from The Empire Strikes Back:

Or this one, from Rogue One:

The information from the droid isn't actually that important. The point is that the line should make you laugh. But I was thinking about how this information is used in the Star Wars universe, and more importantly, where it could be derived from. And I came to an important realization:

These droids must be using Bayesian inference.

It's incredibly unlikely that these probabilities are empirically derived (BTW, this approach of using completely empirical data to derive probabilities is called Frequentism). C-3PO, for instance, says the odds of successfully navigating an asteroid field are 3,720 to 1. What that means is he has to have data on at least 3,721 attempts at navigating an asteroid field. And of course, you'd want more data than that. Just because 1 attempt out of the 3,721 was successful doesn't mean those are the true odds. It's possible the odds are actually 10,000 to 1. You need a lot of data to empirically derive the probability of something.

And what about K-2SO simply saying the probability that Jyn will use the weapon against Cassian is "very high"? It doesn't actually matter what the probability is, but where does that value come from? Sure, it's possible that K-2SO is simply using the probability that an escaped convict would use a weapon on another person, but still, it doesn't seem like there would be a lot of data just laying around. And if K-2SO prefers to use data specific to the situation, he'd need data on the outcome of a very specific situation, one that has likely never happened.

But it isn't unusual for people/droids/whatever to want to know the odds of something that might never have happened before - an event so rare it's impossible to observe it naturally but that you need to be prepared for in the unlikely event that it happens. Insurance companies need to know the potential risks of taking on a new account. Governments need to prepare for potential wars. And scientists need to be able to make causal inferences from their data, sometimes data not collected in such a way to infer cause. To a classical statistician, those puzzles would be difficult, maybe impossible. But to a Bayesian, it is completely possible to generate odds on a thing that has never happened before.

(If you need to refresh your memory on Bayes' Theorem, check out posts here, herehere, and here. And as soon as I learn how to invent more free time, I'm going to sit down and learn Bayesian statistics so I can stop Dunning-Kruger-ing my way through it.)

What K-2SO and C-3PO are generating are conditional probabilities - the probability of something happening given known probabilities about the present situation. These known probabilities are called "priors," and the droid could draw on whatever priors make sense. So C-3PO might be drawing on data about the maneuverability of the Millennium Falcon, the probability of crashes while being pursued, size and motion of the asteroids, and even observations about Hahn Solo himself. Using those conditions, C-3PO can calculate the probability that they'll make it out of the asteroid field alive.

(Side note: Successfully navigating an asteroid field actually wouldn't be that difficult. Check out this post from The Math Dude at Quick and Dirty Tips.)

And just as with the asteroids, K-2SO doesn't need to have the empirical odds that Jyn will use her "found" blaster on Cassian. Instead, he could use known information on Jyn's proclivity toward violence, rates at which convicted criminals use guns, and even probability of a weapon being fired in emotional situations or probability that Cassian will piss Jyn off somehow. K-2SO could use whatever priors make sense, and use that information to derive this "very high" probability.

Hopefully you're as excited as I am about seeing The Last Jedi!

May the Force be with you.

Saturday, December 9, 2017

Must Read: "Cat Person" by Kristen Roupenian

A friend and fellow writer shared this story on Facebook: "Cat Person" by Kristen Roupenian, published in The New Yorker. It's excellently written, and captures some feelings I imagine are very close to home for many women, myself included.

The author, Kristen Roupenian

And if you enjoyed the story as much as I did, I recommend also checking out Deborah Treisman's Q&A with Roupenian:
Your story in this week’s issue, “Cat Person,” is both an excruciating bad-date story and, I think, a kind of commentary on how people get to know each other, or don’t, through electronic communication. Where did the idea for the story come from?

Especially in the early stages of dating, there’s so much interpretation and inference happening that each interaction serves as a kind of Rorschach test for us. We decide that it means something that a person likes cats instead of dogs, or has a certain kind of artsy tattoo, or can land a good joke in a text, but, really, these are reassuring self-deceptions. Our initial impression of a person is pretty much entirely a mirage of guesswork and projection. When I started writing the story, I had the idea of a person who had adopted all these familiar signifiers as a kind of camouflage, but was something else—or nothing at all—underneath.

Do you think that the connection that these two form through texting is a genuine one?

I think it’s genuine enough as far as it goes, but it doesn’t go very far. That Robert is smart and witty is true, but does the fact that someone’s smart and witty mean that he won’t murder you (as Margot wonders more than once), or assault you, or say something nasty to you if you reject him? Of course it doesn’t, and the vertigo that Margot feels at several points in the story is the recognition of that uncertainty: it’s not that she knows that Robert is bad—because if she knew that she would be on solid ground—but that she doesn’t know anything at all.

Anyone Else Want to Be a Cop in New Zealand Now?

New Zealand has just released a hilarious police recruitment video:

The best part? The video features actual New Zealand police officers:
Constable Zion Leaupepe gets the first speaking role in the video, and she’s joined by more than 70 of her colleagues, with police commissioner Mike Bush making a quick appearance as himself. There’s even a police cat briefly glimpsed in the climatic chase for a surprising four-legged crook. And in an especially cheeky touch, the end credits refuse to name the members of the AOS (Armed Offenders Squad) who took part in the shoot.

Friday, December 8, 2017

Videos to Help You Get Through Your Friday

It's been a long week. So I've got some videos lined up to help me get through the day. First up, Postmodern Jukebox brings us this awesome Motown-style cover of "Tomorrow" from Annie; the amazingly talented Shosanna Bean sings lead, and Toni Scruggs and Tiffany Smith provide backup (plus a short interjection of "Hard Knock Life"):

The Room gets songifyed:

Two members of my choir talk about singing Messiah (our first concert is a week from tomorrow!):

Jenny Nicholson talks about playing The Last Jedi Bingo:

Thursday, December 7, 2017

Today's Links

I've got a long day ahead of me today, including a conference call this evening until around 7:30. But here are the links I have sitting open that I'll read/watch/do later:

And I Say I Never Win Anything

Yesterday at work, our building had an ugly sweater party. I entered a drawing, thinking I wouldn't win. But today, I got to work to find a voicemail and email telling me I'd won a gingerbread house! Here it is:

It smells amazing.

Wednesday, December 6, 2017

And the Winner Is...

Time magazine announced the winner of the 2017 Person of the Year: the #MeToo movement:

Time refers to the women behind the movement as "The Silence Breakers." And though this movement has received widespread attention this year, the hashtag was actually started 10 years ago by Tarana Burke.
#MeToo rose to prominence as a social media campaign in the wake of high-profile accusations against Hollywood producer Harvey Weinstein. After actress Alyssa Milano popularized the hashtag, thousands of women began sharing their stories about the pervasive damage wrought by sexual harassment and by "open secrets" about abuse.

The movement's empowering reach could be seen in the platform on which Time announced its choice: the Today show. It was just one week ago that NBC fired the morning program's longtime and powerful co-host, Matt Lauer, over a detailed complaint of "inappropriate sexual behavior in the workplace."

While the most high-profile #MeToo stories have come from women and men who work in the movies and media, the Time article also features women who work hourly jobs, some of whom want to remain anonymous. The magazine's cover portrait includes strawberry picker Isabel Pascual, lobbyist Adama Iwu and former Uber engineer Susan Fowler along with Ashley Judd and Taylor Swift.

Winning Books on Goodreads

Goodreads just announced their winners of Best Books 2017:
There is surprisingly little overlap between this list and Amazon's Top 100. You might remember I lamented that Amazon didn't include The Radium Girls, which won History & Biography here, or What It Means When a Man Falls From the Sky, which was nominated for Best Fiction here, but did not win. In fact, the only books from this list that made Amazon's list were Little Fires Everywhere (2), The Sun and Her Flowers (60), and The Hate U Give (21).

I've added many of these books to my reading list. In fact, The Radium Girls has been sitting on my to-read shelf for months now, and I keep picking up Sleeping Beauties in the bookstore, only to put it back down and tell myself not to buy it until I'm ready to read it. 

I may have a book problem, but I'm kind of okay with it...

Monday, December 4, 2017

The End of Classical Statistics As We Know It? Probably Not

Brian Caffo just released a video discussing something I've been thinking about as I learn more about Artificial Intelligence - will AI take over classical statistics? Do I need to be worried about my job being performed by AI in the future? Check the video out here:

There are a variety of reasons why I think statistics and statisticians/psychometricians will remain useful, even as technology advances. Not only are statistical results more parsimonious and easier to wrap one's head around than machine learning results, but many of the decisions statisticians and psychometricians make are still somewhat subjective.

If I give a dataset to 12 statisticians, along with the questions I want to answer and hypotheses I want to test, I'm likely to get 12 different sets of analyses. Some of the decisions we make are more art than science, or even simply a matter of preference. Some statisticians put more trust in the robustness of statistical results to deviations from the key assumptions. There are a variety of explanations as to why the 12 statisticians would come back with 12 different analysis approaches, though some approaches may be more justifiable than others.

Enjoy the video and let me know your thoughts and reactions!

Sunday, December 3, 2017

Statistics Sunday: Practice Effects and Modern Testing Approaches

In their ground-breaking book, Cook and Campbell introduced us to threats to validity. Remember that validity refers to truth and comes in a four flavors: internal, external, construct, and statistical conclusion. You can learn more about validity at the link, above but as a brief refresher:

  • Internal validity - The effects (dependent variable) observed in the study are caused by the independent variable. Maximizing internal validity means isolating these two variables, so that you can show a true causal relationship between them.
  • External validity - The findings of the study can be generalized. The more control you have over the situation, the higher your internal validity; but this results in lower external validity, because it's difficult to generalize from a highly controlled environment to a less controlled environment.
  • Construct validity - The variables measured in the study actually represent the underlying constructs. We can't hold a tape measure up to your brain to find out your cognitive ability; we have to give you a measure of cognitive ability, which may or may not truly measure the underlying construct.
  • Statistical conclusion validity - The statistical analyses used to draw conclusions have been correctly applied and interpreted. If, for instance, you don't quite meet the assumptions of a test, you weaken your statistical conclusion validity. (That doesn't mean your findings aren't true, just that the probability that they're true is lower than if you fully met the assumptions.)
It would be impossible to design a study that maximizes all four types of validity. You could probably maximize a couple of them at once, but internal and external validity, for instance, involve trade-offs. And any methodological approach you take is going to impact one or more types of validity. These aspects that decrease a type of validity are called threats to validity.

Usually, what we want to get at in our study is to establish a causal relationship between two things. At least, any of us from a field that focuses on experimentation (where independent variables can be manipulated) is interested in establishing causal relationships. We have different methods that we use to try to establish cause. One way that we do this is by taking a sample of people, randomly assigning them to some level of our independent variable and measuring the effect of the IV on the dependent variable.

The problem with this approach, of course, is that the people in the 2 or more experimental groups are different from each other. We do many things to try to equalize groups, but we can never truly know our groups are equivalent.

So another way to handle this is by having one group of people, delivering the different levels of the IV in a randomized order, and measuring the dependent variable after delivering each IV. The problem with this approach is that we could have carryover effects. We don't know if the dependent variable we observe is due to the intervention they just received, or the one they received before that. We can't wipe a person's memory between each segment.

In fact, any time you expose a person to the same measure more than once, you're going to see differences in scores, due to practice. If your intervention is meant to improve performance, simply being exposed to a measure will result in improvements regardless of whether the person received some intervention.

However, there could be a way around this using modern testing approaches, specifically computer adaptive testing (CAT). I'm planning on writing a longer post describing how CAT work, but the short answer is that CATs determine the next item based on your response to the previous item. If you get the item correct, you get a more difficult item. If you get the item incorrect, you get an easier item. 

CATs also use large item banks, so the easy item you receive might be totally different than the easy item I receive, even if they are at the same level of difficulty. What this means is that you're highly unlikely to see the same item twice. And if your ability goes up (or down), you are really unlikely to see the same item twice. That's not to say you might not still observe practice effects, but CAT helps reduce those effects.

Obviously, CAT can't be used for everything and to use it requires many things like access to computers (which may limit how many people you can test at once, depending on available resources), ability to install programs on such computers to deliver CATs, and a large bank of items. But I imagine, over time, as CAT becomes more and more common, we're liable to see more studies using it.

What has been your experience with computer adaptive testing? Or practice effects?

Friday, December 1, 2017

Link Round-Up

Happy Friday, everyone! Here are the tabs I have open, that I've either read or will be reading soon:
  • The closest The Room will ever get to winning an Oscar - the "making of" comedy, The Disaster Artist starring James Franco is getting some Oscar buzz
  • Matt Lauer has commented on the sexual harassment allegations - my favorite part is where he says, "Repairing the damage will take a lot of time and soul searching and I'm committed to beginning that effort. It is now my full time job." Translation: Look how hard I'm working to make it right. And, oh yeah, I'm reminding you that I no longer have a full-time job. Pardon me while I make the "nobody cares" motion. You guys know the one I'm talking about.
  • Speaking of men behaving badly, a friend shared this older article that details the history of Chevy Chase pissing people off, and apparently being racist and sexist. He's Chevy Chase, and I'm not. And for that, I'm thankful.
  • Finally, the APS Observer publishes an article about the hidden costs of sleep deprivation
Also: If you've never experienced The Room and don't really want to watch the entire horrible movie, you can check out Chris Stuckmann's detailed review to get pretty much everything you need to appreciate The Disaster Artist:

Thursday, November 30, 2017

Comments Supporting the Repeal of Net Neutrality Are Likely Fake

Trump loves talking about the "millions of illegal voters." Well, there are millions of pro-repeal net neutrality comments that are very likely fake:
NY Attorney General Schneiderman estimated that hundreds of thousands of Americans’ identities were stolen and used in spam campaigns that support repealing net neutrality. My research found at least 1.3 million fake pro-repeal comments, with suspicions about many more. In fact, the sum of fake pro-repeal comments in the proceeding may number in the millions. In this post, I will point out one particularly egregious spambot submission, make the case that there are likely many more pro-repeal spambots yet to be confirmed, and estimate the public position on net neutrality in the “organic” public submissions.
Below, you can see in highlight some of the phrases that popped up again and again with highly similar syntax, and just a synonym switched out every once in a while:

Also suspect is the fact that the pro-repeal comments are more duplicative than the anti-repeal comments, meaning if these 1.3 million similarly worded comments were coming from grassroots efforts, you should see lots of duplication in comments on both sides. People writing in favor of keeping net neutrality deviate much more from the form letter.

So what truth can be gleaned from these comments?:
It turns out old-school statistics allows us to take a representative sample and get a pretty good approximation of the population proportion and a confidence interval. After taking a 1000 comment random sample of the 800,000 organic comments and scanning through them, I was only able to find three comments that were clearly pro-repeal. That results in an estimate of the population proportion at 99.7%. In fact, we are so near 100% pro net neutrality that the confidence interval goes outside of 100%. At the very minimum, we can conclude that the vast preponderance of individuals passionate enough about the issue to write up their own comment are for keeping net neutrality.

Uptown Rats

I have a confession: I'm a rat lover. I had a pet rat named Lily in college, and she was the best pet I'd ever had: personable, smart, absolutely adorable. Don't get me wrong, I wouldn't snuggle up to a sewer rat - Lily was as good as she was in part because she was the offspring of lab rats and also because she was highly socialized (exposed to people) from the very beginning of her life.

Still, I find this study really cool:
For the past two years, [Fordham University graduate student, Matthew] Combs and his colleagues have been trapping and sequencing the DNA of brown rats in Manhattan, producing the most comprehensive genetic portrait yet of the city’s most dominant rodent population.

As a whole, Manhattan’s rats are genetically most similar to those from Western Europe, especially Great Britain and France. They most likely came on ships in the mid-18th century, when New York was still a British colony.

When Combs looked closer, distinct rat subpopulations emerged. Manhattan has two genetically distinguishable groups of rats: the uptown rats and the downtown rats, separated by the geographic barrier that is midtown. It’s not that midtown is rat-free—such a notion is inconceivable—but the commercial district lacks the household trash (aka food) and backyards (aka shelter) that rats like. Since rats tend to move only a few blocks in their lifetimes, the uptown rats and downtown rats don’t mix much.
To collect this genetic information, Combs and colleagues trapped rats - using an enticing bait made from peanut butter, bacon, and oats - and collected tissue samples, mostly from the tail. He also collected information on rats' locations, by crowd-sourcing data:

Fortunately, it sounds like Combs has come to appreciate rats as well:
After two years of trapping rats, Combs has come to respect the enemy. At the end of our conversation, he launched into an appreciation of rats—their ability to thrive on nearly anything, their prodigious reproduction, and their complex social structure, in which female rats will give birth all at the same time and raise their offspring in one nest. “They are, quote-unquote, vermin, and definitely pests we need to get rid of,” he says, “but they are extraordinary in their own ways.”

Wednesday, November 29, 2017

Winner, Winner, Chicken Dinner!

That's right, kids, I did it:

This was my third NaNo and my second win. Phew!

America's Existential Dread, Expressed as Christmas Decorations

If Melania Trump was trying to express how terrified we all are over the state of the country in Christmas decorations, she has succeeded admirably:

And if you'd like to see more hilarious reactions to This Nightmare Before Christmas (Part 2, I believe, since Part 1 was the day after Election Day 2016), Bored Panda was kind enough to compile this list.

Statistical Sins: When the Data are Too Perfect

Yesterday, Ars Technica published an article about an investigation into the research of Nicolas Guéguen, a psychologist who has received a great deal of media attention for his shocking findings in gender effects. His research includes findings that men prefer women in heels or wearing red, and that men are more likely to help a woman wearing her hair down instead of up. But, according to James Heathers and Nick Brown, Guéguen's data and high publication rate are suspect:
What they've found raises a litany of questions about statistical and ethical problems. In some cases, the data is too perfectly regular or full of oddities, making it difficult to understand how it could have been generated by the experiment described by Guéguen.

Social media is where it all kicked off, when Nick Brown saw a tweet about a paper claiming that men were less likely to help a woman who had her hair tied up in a ponytail or a bun. When they looked more closely at the paper, something odd jumped out at them: the numbers in the paper looked strangely regular.

When you’re dividing by three, the decimal points will always follow this pattern: either .000, .333, or .666. If you divide by 30, the pattern just moves up a decimal place: the second decimal will always be 3 or 6.

In this study, every average score was divided by 30, because each group (male-ponytail, male-loose, female-bun, and so on) had 30 people in it. But every average number was perfectly round: 1.80, 2.80, 1.60. That’s … unlikely. “The chance of all six means ending in zero this way is 0.0014,” write Heathers and Brown in their critique.
Many of Guéguen's studies involve elaborate situations using confederates - research assistants who pretend to be a participant or random person on the street. But Guéguen publishes many single author papers without acknowledgements. When Heathers and Brown reached out to Guéguen for more information on how he could publish so many elaborate studies on his own, he explained that he supervises many student projects. But why aren't the students at least thanked in the papers? Or listed as a coauthor, as they should be if they're doing a great deal of the work?

Heathers and Brown have repeatedly reached out to Guéguen for some documentation to substantiate that these studies occurred as described, but email correspondence, ethics review committee reports, and original datasets have not been shared in many cases.

Brown will be publishing the results of his and Heathers's examination of Guéguen's work on his blog. The first critique can be found here.

As has happened before, this particular instance of alleged academic dishonesty is liable to lead to a discussion about the problems of the "publish or perish" mentality in academia and research. But, as in previous cases, it's unlikely that such a discussion will result in any real improvements to dissuade such honesty. When the benefit of publishing a great deal is high and the probability of being caught is low, these things will continue to happen. Completely fabricated research is rare and likely to remain so, but tiny slips into academic dishonesty - massaging numbers or dropping cases - will happen, even by the most honest of researchers.

Tuesday, November 28, 2017

So Close!

After falling dangerously behind, I've been able to play catchup on NaNoWriMo. Here's my current stats:

I only need to write 3600ish more words between now and midnight Thursday. I can do this!

It's a-Me!

Fellow writer, Katie Roman, invited members of the Chicago NaNoWriMo to answer some interview questions. Today, my "Author Spotlight" is up! I got to talk about my love Ray Bradbury and Margaret Atwood, why self-doubt is public enemy number 1 to my writing, and why bars are better than coffee shops for people-watching and writing inspiration. Plus, we were asked to share a meme that describes our writing. Here's mine:

Monday, November 27, 2017

Statistics Sunday: Data Discovery

For today's (late) Statistics Sunday post, I was going to dig into FiveThirtyEight's Thanksgiving data, to find the real reason people in the West eat so much salad at Thanksgiving. As I was inspecting the data and readme file, I clicked back in the directory and found that FiveThirtyEight has shared a ton of data on GitHub. So instead of analyzing Thanksgiving data, I clicked through readme files of other data they had available.

Yes, I became distracted by new data.

Some favorites among the list:

Friday, November 24, 2017

Statistical Sins: Thanksgiving Edition

Hopefully you had a wonderful Thanksgiving. I ended up traveling yesterday, and that combined with some illnesses in my family means I still haven't gotten the "official" Thanksgiving meal (turkey, sweet potatoes, etc.). That should happen tomorrow.

On the subject of Thanksgiving, FiveThirtyEight recently re-released that results of a survey from a couple years ago, showing the most disproportionately consumed Thanksgiving side dish:
And yes, I can say Kansans love their casseroles, especially green bean casserole. I've been to family Thanksgivings where there have been, I kid you not, 3 different versions of green bean casserole.

But what people are really reacting to is that the West coast just loves their salad:
But some savvy Tweeters are suggesting that this could be an issue with the survey itself or analysis of the resulting data:
Want to try to answer this question yourself? FiveThirtyEight was kind enough to share the data on GitHub. Once I get some writing done, I might have to dig into this dataset.

Tuesday, November 21, 2017

If You Want to Terrify Your Family...

... you could make this roasted face hugger for Thanksgiving:

This dish is created with a whole chicken, snow crab legs, and chicken sausage. And it's only slightly more repulsive than the unholy mashup that is turducken.

Life in an Iron Lung

Those who forget the past are doomed to repeat it. And one of the situations where that aphorism is absolutely true is when it comes to vaccines. I feel very strongly about this topic, so strongly that I will feed the trolls even when I shouldn't and have unfriended people on Facebook who refuse to vaccinate their children.

No one dies from autism (or whatever anti-vaxxers are currently concerned about). But people die from preventable diseases that have been eradicated by vaccines all the time. Tens of thousands of people die from seasonal influenza each year. During the Spanish flu epidemic in 1918, 50-100 million people died from a strain of H1N1 - the same strain that reemerged in 2009 (the "Swine flu"). And other conditions we are vaccinated for, like the measles or diptheria, are far deadlier.

One of the most debilitating conditions for which we can vaccinate is poliomyelitis, or polio. People who survived polio went on to have a variety of complications, ranging from mobility limitations and muscle weakness to paralysis and severe difficulty breathing. Yesterday, Jennings Brown for Gizmodo published a story about 3 polio survivors who use an apparatus called an iron lung that helps them breathe. Two of them use the iron lung while they sleep, so they don't stop breathing in the night. One of them uses the iron lung nearly all the time.

The stories these 3 told were heart-breaking:
When [Martha] Lillard was a child, polio was every parent’s worst nightmare. The worst polio outbreak year in US history took place in 1952, a year before Lillard was infected. There were about 58,000 reported cases. Out of all the cases, 21,269 were paralyzed and 3,145 died.

Children under the age of five are especially susceptible. In the 1940s and 1950s, hospitals across the country were filled with rows of iron lungs that kept victims alive. Lillard recalls being in rooms packed with metal tubes—especially when there were storms and all the men, women, adults, and children would be moved to the same room so nurses could manually operate the iron lungs if the power went out. “The period of time that it took the nurse to get out of the chair, it seemed like forever because you weren’t breathing,” Lillard said. “You just laid there and you could feel your heart beating and it was just terrifying. The only noise that you can make when you can’t breathe is clicking your tongue. And that whole dark room just sounded like a big room full of chickens just cluck-cluck-clucking. All the nurses were saying, ‘Just a second, you’ll be breathing in just a second.’”
The polio vaccine nearly eradicated cases in the United States. There are still cases in parts of the world where vaccines are not readily available. The article comments that if one infected person were to visit Orange County, California, where many parents are opting out of vaccination, we could have a polio epidemic in the US for the first time in decades.

New iron lungs haven't been manufactured in many years, and new parts aren't available any longer either. In the article, the author observes that these 3 polio survivors are fortunate to have mechanically oriented people in their lives who have fixed and maintained their iron lungs. Iron lung users who were unable to find people with these skills died as a result.

The most important message in this article, spoken by experts as well as the 3 survivors is vaccinate:
But another thing they all had in common is a desire for the next generations to know about them so we’ll realize how fortunate we are to have vaccines. “When children inquire what happened to me, I tell them the nerve wires that tell my muscles what to do were damaged by a virus,” Mona [Randoloph] said. “And ask them if they have had their vaccine to prevent this. No one has ever argued with me.”

[Paul] Alexander told me that if he had kids he would have made sure they were vaccinated. “Now, my worst thought is that polio’s come back,” he said. “If there’s so many people who’ve not been—children, especially—have not been vaccinated... I don’t even want to think about it.”

Lillard is heartbroken when she meets anti-vaccine activists. “Of course, I’m concerned about any place where there’s no vaccine,” she said. “I think it’s criminal that they don’t have it for other people and I would just do anything to prevent somebody from having to go through what I have. I mean, my mother, if she had the vaccine available, I would have had it in a heartbeat.”

Monday, November 20, 2017

Video RoundUp

Once I have some free time later this week, here are some videos on my "To Watch" list:

Sunday, November 19, 2017

Statistics Sunday: What Are Cognitive Interviews?

In a recent Statistical Sins post, I talked about writing surveys and briefly mentioned the concept of cognitive interviews. Today, I wanted to talk a little more about what they are and how they can be used to improve surveys and measures.

The purpose of a cognitive interview is to get into the mind of the individual, to understand their thought process. When conducting cognitive interviews for a survey or measure, your goal is to understand the respondents' thought process while completing the instrument.

There are two different ways you can conduct these interviews. The first is the think aloud technique. As the respondent completes the measure, you ask him or her to narrate the thought process. As I said in the statistical sins post, when people encounter a measure, they read an item and then determine their response. They then compare their internal response to the options given, and in essence translate their personal response to a supplied answer.

The problem I've encountered is that people can't always verbally narrate what they're thinking. Thought isn't always in words and sentences, and to communicate those thoughts, one must first translate nonverbal information into verbal information. Even when I want to do a think aloud cognitive interview, it will probably use elements of the second approach, direct probing.

For this approach, the researcher asks the respondent questions while he or she is looking at your instrument - specific questions that get at how they're approaching the instrument, and ways it could be improved. For instance, you might ask respondents what they think about when they hear a particular word or phrase that shows up in your instrument. This can help you identify potential misunderstandings or other words/phrases that would be more clear. You might ask if they like the response options and whether any options are missing. You could even ask if important items or questions are missing.

It is possible to do a mixture of the two. For example, I may have a person go through a measure while narrating their thoughts. I'll usually have a general question to help people keep narrating as they complete the measure - basically questions I use if they lapse into silence. Then, I'll have more targeted questions to help get specific responses to issues I care most about. Anytime I do interviews, I like to go from general to more specific topics, so this approach to cognitive interviews works well for me.

This approach should happen further along in the development of your survey or measure. If you're stil trying to figure out what should go on your instrument, you'd be best to use different methods like literature reviews, convening an expert committee, or focus groups. But once you get father along in your process, cognitive interviews are an important way to make sure you get good data from the instrument you've exerted a lot of effort to create.

Friday, November 17, 2017

Candy is Dandy But Liquor is Quicker

In yesterday's Chicago Tribune, Josh Noel longs for the day when beer tasted like beer:
After six hours wandering the aisles of the Festival of Wood and Barrel-Aged Beer last weekend, I have concluded that craft beer is betraying itself. It is forgetting what beer should taste like.

Though FOBAB, held this year at the University of Illinois at Chicago Forum on Friday and Saturday, remains Chicago’s most essential beer festival, corners of it have become a showcase for beer that tastes more like dessert than beer. “Pastry stouts,” the industry calls them.

Among the 376 beers poured at FOBAB this year, about 50 were pastry stouts, the largest share of the largest category at FOBAB. [These] beers are overrun with coffee, vanilla beans, coconut, cinnamon, chiles and cacao nibs.

So very many cacao nibs.

It’s a confounding moment in craft beer. The industry is still growing rapidly — 6,000 breweries operating and hundreds more in planning — and the race is on for differentiation. The problem is that the differentiation is seeming both too sweet and too repetitive.
I'll admit, I'm torn on this issue. I completely agree that many of these beers are far too sweet, not really tasting "like beer" but more like flavored syrup. But this isn't a new phenomenon. For instance, look at Reinheitsgebot, the German Beer Purity Laws, which were established centuries ago. I'm sure many of the beers Noel holds up as examples that "taste like beer" would fail to pass these purity laws.

If we go back to the very beginning of brewing - and that's a long way back, because beer is one of the first beverages humans produced; it even predates wine by about 2000 years - the first drinks we call beer are absolutely nothing like what we have today, brewed from barley and different kinds of grain. What makes something beer isn't so much about flavor or even the precise ingredients, but the process used to make it.

One of the reasons I love beer is the nuances of flavor. The many different varieties of beer have resulted from centuries of innovation. And I'm sure as each new style was invented, someone was lamenting for the days when the beer tasted like beer. While I may not love the pastry stouts, and instead prefer beers that are more dry or bitter, I don't see any issue with this new trend. It highlights exactly what I love about beer - variety.

Besides, I'll try anything barrel-aged, even if I worry it will be too sweet for me.

Thursday, November 16, 2017

Finding Utopia

After an especially long workday yesterday, that started around 8:30 am and ended around 8:00 pm, I decided to grab a burger and a beer at one of my favorite beer gardens. Not only did I get to catch the end of the Hawks game, I was thrilled to see that they had Samuel Adams Utopias 2017 on their draft menu.

You might remember I posted about this beer a little over a week ago. This 2 ounce pour cost me $26, much cheaper than picking up my own bottle for $199 - not to mention, I'd need a bunch of friends to share that bottle with, since this beer comes in at 28% ABV. This is a beer one should sip slowly, and I did - my dinner companion finished an entire pint in the time it took me to finish my 2 ounces.

The beer was lovely: rich, malty, and slightly sweet. It's served room temperature, as most cask-aged beers are, and is not carbonated, because of the high alcohol content. If you're a beer-lover and get the chance to try it, I highly recommend it.

Wednesday, November 15, 2017

Whoa, We're Halfway There

Reached (and surpassed) 25,000 words:

And because I just can't leave a song lyric unfinished:

NaNoWriMo Hump-Day: Some Resources for Day 15 (And Beyond)

We're reaching the midpoint of NaNoWriMo - and on a Wednesday, so today is like a Super Hump-Day. By the end of today, I plan to have at least 25,000 words written if it kills me. So if you too need help to get through the humpiest of all hump-days, here are some resources:

  • When your writing is just too "very," this list gives you replacements for "very + [adjective]"
  • Speaking of the middle of things, here's some advice on giving some love to the middle child of your novel, the middle act
  • Jeff Goins says the way to be a good writer is practice, practice
  • Daily Writing Tips pens a list: 40 shades of -ade
  • And if you're feeling self-doubt about how you could possibly write a novel [raises hand], know you're in good company

Tuesday, November 14, 2017

Where Serendipity Takes Me

I'm having one of those evenings where you feel like there is some strange force in the universe guiding you. Generally I don't believe in that kind of thing. But every once in while, things like this happen in a chain that feels anything but random.

It's Tuesday, when I have belly dance class, which means I drive to work and head up to Evanston after the workday.

On my way to Evanston, one of my favorite songs ever comes on the radio: Head Over Heels by Tears for Fears. It was on a station I don't normally listen to but I had switched over due to an incredibly annoying ad on my usual station.

I realize I'm craving tacos so I stop in one of my favorite restaurants where I run into two good friends and have some delicious chicken tacos.

On my way out, I stop to pet a very sleepy 8-week-old golden retriever.

I round the corner to my class... and see the lights are dark and a closed sign is on the door. I think about returning to the taco place but instead decide to head to my favorite tap room.

So now I'm writing and listening to an Irish folk duo while sipping beer.

Life is good.

A Face Only a Mother Could Love

A couple of prehistoric sea creatures appeared recently, one off the coast of Portugal and the other in the harbors of Sydney, Australia. And they've got faces you just can't unsee:


In fact the frilled shark, which has 25 rows of nasty big pointy teeth, is a living fossil, because remains of this creature have been found dating back as much as 80 million years.

Our planet is both cool and terrifying.

Monday, November 13, 2017

Statistics Sunday: What is Bootstrapping?

Last week, I posted about the concept of randomness. This is a key concept throughout statistics. For instance, I may have mentioned before but many statistical tests assume that the cases used to generate the statistics were randomly sampled from the population of interest. That, of course, rarely happens in practice, but this is a key concept in what we call parametric tests - tests that compare to an assumed population distribution.

The reason for this focus on random sampling goes back to the nature of probability. Every case in the population of interest has a chance of being selected - an equal chance in simple random sampling, and unequal but still predictable chances when more complex sampling methods are used, like stratified random sampling. It's true that you could, by chance, draw a bunch of really extreme cases. But there are usually fewer cases in the extremes.

If you look at the normal distribution, for instance, there are so many more cases in the middle that you have a much higher chance of drawing cases that fall close to the middle. This means that, while your random sample may not have as much variance as the population of interest, your measures of central tendency should be pretty close to the underlying population values.

So we have a population, and we draw a random sample from it, hoping that probability will work in our favor and give us a sample data distribution that resembles that population distribution.

But what if we wanted to add one more step, to really give probability a chance (pun intended) to work for us? Just as cases that are typical of the population are more likely to end up in our sample, cases that are typical of our sampling distribution are more likely to end up in a sample of the sample. (which we'll call subsample for brevity's sake) And if we repeatedly drew subsamples and plotted the results, we could generate a distribution that gets a little closer to the underlying population distribution. Of course, we're limited by the size of our sample, in that our subsamples can't exceed that size, but we can bypass that by random sampling with replacement. That means that after pulling out a case and making a note of it, we put it back into the mix. It could get drawn again. This gives us a theoretically limitless sample from which to draw.

That's how bootstrapping works. Bootstrapping is a method of generating unbiased (though it's more accurate to say less biased) estimates. Those estimates could be things like variance or other descriptive statistics, or it could be used in inferential statistical analyses. Bootstrapping means that you use random sampling with replacement to estimate values. Frequently, it means using your observed data as a sort of population, and repeatedly drawing large samples with replacement from that data. In our Facebook use study, we used bootstrapping to test our hypothesis.

To summarize, we measured Facebook use among college students, and also gave them measures of rumination (tendency to fixate on negative feelings), and subjective well-being (life satisfaction, depression, and physical symptoms of ill health). We hypothesized that rumination mediated the effect of Facebook use on well-being. Put in plain language, we believed using Facebook made you more likely to ruminate, which in turn resulted in lower well-being.

The competing hypothesis is that people who already tend to ruminate use Facebook as an outlet for rumination, resulting in lower well-being. In this alternative hypothesis, Facebook is the mediator, not rumination.

Testing mediation means testing for an indirect effect. That is, the independent variable (Facebook use) affects the dependent variable (well-being) indirectly through the mediator (rumination). We used bootstrapping to estimate these indirect effects; we took 5000 random samples of our data to generate our estimates. Just as we're more likely to draw cases typical of our sample (which are hopefully typical of our population), we're more likely to draw samples that (hopefully) have the typical effect of our population. The resulting indirect effects we get from bootstrapping won't be the same as a simple analysis of our observed data. We're using probability to remove bias from our estimates.

And what did we find in our Facebook study? We found stronger support for our hypothesis than the alternative. That is, we had stronger evidence that Facebook use leads to rumination than the alternative that rumination leads to Facebook use. If you're interested in finding out more, you can read the article here.

Friday, November 10, 2017

Amazon Editors' Top 100 Books

It's November, everyone is already talking about winter holidays, and I've already been forced to listen to hours of Christmas music. But one thing that I like about this time of year is the various year-end lists. As I mentioned before, Goodreads is having its members vote for the Best Books of 2017. I'll be sharing their final list when voting ends. In the meantime, here's the top 100 books of the year according to Amazon's editors:

  1. Killers of the Flower Moon: The Osage Murders and the Birth of the FBI
  2. Little Fires Everywhere
  3. Beartown
  4. Exit West
  5. Homo Deus: A Brief History of Tomorrow
  6. Lincoln in the Bardo
  7. The Heart's Invisible Furies
  8. You Don't Have to Say You Love Me
  9. Sourdough
  10. The Dry
  11. The Lost City of the Monkey God: A True Story
  12. My Absolute Darling
  13. Ginny Moon
  14. The Ministry of Utmost Happiness
  15. Priestdaddy
  16. Spoonbenders
  17. 4 3 2 1
  18. This Is How It Always Is
  19. American Fire: Love, Arson, and Life in a Vanishing Land
  20. Turtles All the Way Down
  21. The Hate U Give
  22. Pachinko
  23. Sing, Unburied, Sing
  24. Option B: Facing Adversity, Building Resilience, and Finding Joy
  25. The Bear and the Nightingale
  26. The Weight of Ink
  27. The Impossible Fortress
  28. Standard Deviation
  29. One of the Boys
  30. Manhattan Beach
  31. Learn Better: Mastering the Skills for Success in Life, Business, and School, or, How to Become an Expert in Just About Anything
  32. Rabbit: The Autobiography of Ms. Pat
  33. Stay with Me
  34. Ghosts of the Tsunami: Death and Life in Japan's Disaster Zone
  35. Leonardo da Vinci
  36. The Book of Dust: La Belle Sauvage
  37. Hue 1968: A Turning Point of the American War in Vietnam
  38. Prussian Blue
  39. The Bright Hour: A Memoir of Living and Dying
  40. Goodbye, Vitamin
  41. Home Fire
  42. Ranger Games: A Story of Soldiers, Family and an Inexplicable Crime
  43. The Burning Girl
  44. A Legacy of Spies
  45. Hunger: A Memoir of (My) Body
  46. Endurance: A Year in Space, A Lifetime of Discovery
  47. Lighter Than My Shadow
  48. Savage Country
  49. The Road to Jonestown: Jim Jones and Peoples Temple
  50. Emma in the Night
  51. History of Wolves
  52. Word by Word: The Secret Life of Dictionaries
  53. Reservoir 13
  54. Long Way Down
  55. Lillian Boxfish Takes a Walk
  56. Good Me Bad Me
  57. In the Great Green Room: The Brilliant and Bold Life of Margaret Wise Brown
  58. Norse Mythology
  59. Testosterone Rex: Myths of Sex, Science, and Society
  60. The Sun and Her Flowers
  61. Coming to My Senses: The Making of a Counterculture Cook
  62. Human Acts
  63. Universal Harvester
  64. The Upstarts: How Uber, Airbnb, and the Killer Companies of the New Silicon Valley Are Changing the World
  65. Insomniac City: New York, Oliver, and Me
  66. Irresistible: The Rise of Addictive Technology and the Business of Keeping Us Hooked
  67. All Grown Up
  68. Apollo 8: The Thrilling Story of the First Mission to the Moon
  69. The Rules Do Not Apply: A Memoir
  70. American War
  71. The Golden House
  72. One Day We'll All Be Dead and None of This Will Matter: Essays
  73. Vacationland: True Stories from Painful Beaches
  74. Sycamore
  75. American Kingpin: The Epic Hunt for the Criminal Mastermind Behind the Silk Road
  76. When the English Fall
  77. The Jersey Brothers: A Missing Naval Officer in the Pacific and His Family's Quest to Bring Him Home
  78. The Floating World
  79. The City of Brass
  80. The Retreat of Western Liberalism
  81. Sting-Ray Afternoons: A Memoir
  83. An American Family: A Memoir of Hope and Sacrifice
  84. What We Lose
  85. Trajectory: Stories
  86. My Favorite Thing Is Monsters
  87. The Last Cowboys of San Geronimo
  88. Mrs. Fletcher
  89. Draft No. 4: On the Writing Process
  90. The Twelve-Mile Straight
  91. A Brief History of Everyone Who Ever Lived: The Human Story Retold Through Our Genes
  92. Sticky Fingers: The Life and Times of Jann Wenner and Rolling Stone Magazine
  93. White Tears
  94. Prairie Fires: The American Dreams of Laura Ingalls Wilder
  95. Principles: Life and Work
  96. The Seven Husbands of Evelyn Hugo
  97. The Vanity Fair Diaries: 1983 - 1992
  98. In the Midst of Winter
  99. Void Star
  100. Magpie Murders
Sadly, I've only read one of the books on the list: Norse Mythology, by Neil Gaiman. This makes me slightly sad, not only because I've clearly missed some great books published this year, but also because some of the wonderful books I read this year didn't make the cut. Where's Lesley Nneka Arimah's What It Means When a Man Falls From the Sky or Kate Moore's The Radium Girls?