My Trump Administration Conspiracy Theory

Trump has done many things that leave us scratching our heads. The most recent being, of course, firing FBI Director James Comey. Despite praising Comey on the campaign trail with regard to the investigation in Hillary Clinton's email server, Trump insists he's fired for the very same reason.

Cue Kellyanne Conway spouting more alternative facts...

And Sean Spicer literally hides (in bushes) to avoid the press:

Oh yeah, and Trump also updated the header on his Twitter feed, to a tweet saying there was "'no evidence' of collusion w/ Russia." It's like the equivalent of a cartoon bad guy putting a sign in front of his secret lair that says, "Not secret lair" on it.

Many have pointed out that if Trump wanted to get away from the allegations about his involvement with Russia, he just did the worst thing he could do: he fired the guy who was investigating these allegations. But I have an alternate theory that explains much of his inexplicable behavior. All of this makes sense if Trump is running his presidency like a reality show. Seriously, this stuff is batshit insane but it makes for great TV. Conway and Spicer and Betsy DeVos (to name a few) are completely incompetent, but they're entertaining - just the kind of characters you'd want to have on your reality show. Even keeping Sally Yates on, rather than having an interim replacement, makes sense if you want to create TV show-style conflict.

The infighting, the Twitter rants, the namecalling - it's reality TV.

What did we expect when we elected a reality TV celebrity? But as entertaining as this may be at one level, it is terrifying at another (let's face it, it's terrifying at every other level). Because America is not a reality show. And I'm worried about what this country will look like when Trump is finally out of office.

Dear god I hope he's removed before his term is up. I can't do 4 years of this. And the "you're fired" memes might just make these insane 100+ days almost worth it.

BTW, when everyone was going nuts about the Comey firing, the Director of the Census Bureau resigned. And that's not good. So Trump could add being the president who f***ed up the census as one of his contributions.

Bonus Statistics Post #1: A Note on Notation

So I've decided to just go for it and write some additional posts on statistical topics. If you have any questions or requests, you can add them in the comments below. Statistics are everywhere and you're welcome to share articles or topics and just ask for clarification or some analysis. Honestly, ask about anything statistical.

I think everyone has something that they are really into, really fixated on, that almost no one else cares about. Not only will they geek out about it, they get really frustrated when they perceive people approaching it in the wrong way. For me that thing is statistical notation. Yeah, I know, kind of weird. I doubt there are many other people who care as much about statistical notation. And I get really frustrated about the lack of (or perhaps perceived lack of) standardized notation in statistics.

Why do I care about this? I think for the same reason I love statistics - I appreciate rules. When dealing with variables and operators and slopes, rules help me understand how they should behave. It allows me to concretize abstract concepts. Statistics is very much about rules. For many statistical tests, we call them assumptions - the rules you have to follow if you want to use that statistical test with your data. I like rules. At least when it comes to math.

In the rest of my life, the only rule is there are no rules... I'm not at all convincing, am I?

To give you an idea about the issue, in graduate school, I was a teacher's assistant for an undergraduate statistics course and a student in a graduate statistics course at the same time. Both classes used a textbook by the same author. I won't name names because I'm a nobody and can't afford to throw shade at a somebody just yet. But same author, no co-authors, and basically the same topics covered, though the graduate one was a bit more in-depth.

The notation wasn't even consistent between these two books.

I didn't realize this until I was asked to lecture for the undergraduate class, and had to deal with many confused looks from students when the notation I wrote on the board was different from what was in their books.

This all could have been solved if we had freaking standardized notation. Just sayin'.

But I'd been thinking about this, now that I'm toying with the idea of writing a book about statistics. I've always had my own philosophy about notation; I've just never articulated it before.

I think the most important component of my approach to notation is to keep in mind what we are trying to accomplish. Whenever we collect and analyze data, we are using that data to represent populations. Sometimes that data comes from whole populations, and sometimes it comes from samples. As far as I've seen, the usual approach is using Greek letters to represent population values (what we call parameters). It's the sample values (what we call statistics) that are notationally inconsistent (even within a single author apparently). But when we collect data from a sample, we are using them as a stand-in for the population. Statistics notation should reflect that connection back to parameters. My approach then is to use the equivalents of the Greek letters:

For instance, population standard deviation is represented by σ (sigma), so I use s to symbolize the sample standard deviation. (Always lowercase because, as you'll learn as you dig into statistics, capital letters also have a separate meaning.)

I've always felt this way about notation, but I really thought about it much more when I started learning structural equation modeling. Just like statistics more generally, there are multiple notation techniques in SEM. However, unlike statistics, there are a limited number of notational approaches and they're all clearly labeled (e.g., the LISREL approach, the M-Plus approach, etc.). I learned LISREL, which uses many of the Greek letters you see above, and this is the approach I prefer, in part because it's how I learned and we tend to think the first way is the right way (a primacy effect) but also in part because I recognize the connection between the analyses I conduct and the Greek letters that tie those analyses back to the population values we're attempting to estimate.

I'm mostly sharing this information to explain why I approach it the way I do and also as a warning that if you decide to learn statistics, the notation you learn may be wildly different depending on whose book you read, and I, for one, think that's a travesty. And as I continue writing about statistics, I'll probably start dropping in some notation. I'll explain that notation when I get there, but this will at least help you understand why I do it the way I do.

Darrow and Bryan Face Off Once Again

You may remember the Scopes trial of 1925, in which Clarence Darrow defended science teacher John Scopes, who was accused of illegally teaching evolution to his students. Though the State of Tennessee, represented by William Jennings Bryan, won that case, Scopes's verdict was overturned on a technicality.

A statue of Bryan was placed outside the Rhea County Courthouse in 2005.

Now, funds are being raised for a Darrow statue to join Bryan's:

Bill Dusenberry, an Americans United activist in Oklahoma, told Tulsa World last year he was spurred to raise support and funds for a Darrow statue to join Bryan’s after a visit to Dayton in 2009. (See “Darrow In Dayton?” September 2016 Church & State.)

“It was obvious to me when I saw that he was not represented, that I needed to do my best to do something about it,” Dusenberry told the newspaper.

Philadelphia sculptor Zenos Frudakis told the Chattanooga Times Free Press that he plans to install his Darrow statue on July 13. A dedication will be held July 14, the day the Scopes Trial Play and Festival begins.

The A to Z of Statistics Revisited

April is becoming a distant memory as we move farther into May. But today, the folks at the Blogging A to Z challenge have asked us to take a moment to reflect on April's blogging. I wanted to do that reflecting right away (May 1) but they set the date for our reflection post one week after that. I almost cheated and posted my reflection whenever I felt like it - what? I'm impatient - but I forced myself to wait so that I had time to think and gather my thoughts.

I see why waiting a week is a good idea, because in that time, I not only reflected on what I'd done thus far, but started considering where to go from here. So here goes...

I was a little concerned about my theme and seriously questioned it up to and even after I shared it on my blog. Not because I didn't love it - I'm not lying when I say that statistics is my first love - but because I wasn't sure I could sell it. As in, I didn't think I could write 26 blog posts about statistics that were enjoyable for anyone to read except me. I even thought about changing my theme after my theme reveal post. But I decided to stay the course, and I'm glad I did.

April renewed my love for statistics and especially renewed my love for teaching and writing about statistics. I'd been toying with the idea of writing a textbook, specifically on Rasch, the measurement model I use in my job, but had never really thought about writing a book on statistics more generally. Now I'm thinking about it - putting together an easy to read book that teaches people the basics of statistics. And because so many stats books and classes simply teach the math, I'd like my book to focus on what I focused on in my posts - the conceptual basis. Not just how to get the numbers but what they mean. Of course, there's already a great book out there that does this:

but at a higher level. In fact, this book was used in my graduate statistics course as a companion to the textbook. My contribution would be more like the primer that gets you closer to being able to really enjoy Abelson's book but that also serves as an introduction to statistics.

In fact, the folks at Blogging A to Z want bloggers to use this month as a springboard into a larger project, such as a book. But I'd just participated because I liked the challenge, not for any kind of inspiration. So this is a pretty surprising revelation.

Why am I thinking about doing this?

1. I really do love statistics. 
2. I've been thinking about other stats topics I couldn't fit into A to Z that I'd like to sit down and write about.
3. As I would tell my statistics students (most of whom only took the class to fulfill a requirement and were certain they were going to hate it) on the first day of class, we encounter statistics all the time. Without some kind of numerical (and statistical) literacy, we're forced to accept or ignore the information people are giving us. But with knowledge of statistics, we can evaluate and determine whether (and what part of) the information is valid and acceptable or not. We don't have to default to just accept or ignore.

My dream? A world where people aren't afraid of numbers and don't panic when they see Greek letters. I seriously had students who would shut down the minute they saw any kind of Greek symbols in their textbook; statistics is full of them. But these letters are just symbols that stand for a concept. We deal with symbols all the time. We don't freak out when we see a stop sign, even though this is also a symbol that communicates a great deal of information: stop, look both ways, yield to other cars, and so on. And these statistical symbols aren't simply abstract concepts - they stand for real things, even if that real thing (like the population mean) is something we're not able to measure ourselves. These aren't imaginary numbers. They really do exist somewhere out there.

Clearly, I learned a lot of things about myself this month. I also learned from my previous Blogging A to Z experience by writing more posts ahead of time and scheduling them. (As with last year, I wrote a list of topics ahead of time, so I wouldn't be struggling to figure out what to write as the month went on.) I didn't do as much writing ahead of time as I could/should have. But when I found myself getting really excited about an upcoming post, I would sit down and write it. If it was ready to go, I'd schedule it. If it still needed more work, I'd save it as a draft to revisit later.

Next year, I hope to do more of this, especially because this year, I posted two posts late. I got to them eventually and didn't give up, but I still had to write and share two posts on a Sunday to get back on schedule after having missed a Friday and Saturday post. This happened on a really busy weekend. Not a big deal, but I know I can stick with the schedule with a bit more pre-planning, especially because this particular busy weekend is a regular event happening every April.

Thanks to everyone who read and commented on my posts! Once again, I didn't do this as much as I should have, but I did discover some new fun blogs through the challenge that I now follow, including Dena's Ramblings (Dena blogged about US Supreme Court cases in April) and Fangirl Stitches (who cross-stitched her way through the Buffyverse).

Whether or not I go through with the book idea, I would love to write more posts about statistics. Some thoughts I have: effect sizes, parametric versus nonparametric tests (terms that I didn't really understand fully until recently, despite taking 10+ statistics courses and years of working in the field), estimation techniques and what they mean, and the general linear model. But I would love to hear what readers would like to know more about, whether it's a topic I didn't cover in April or a more in-depth examination of a previous topic. Any requests?

Concert Weekend

I'm busily finishing up a project for work, so I haven't had time to blog the last couple days. But I'm in for a busy weekend; my choir has two concerts, one tonight and another Sunday.

The concert will feature works and performance by composer Jeff Beal, who has composed music for many shows and movies. In fact, I'm working from home today and have one of the shows he's composed (and won an Emmy) for, House of Cards, on in the background. His wife, soprano Joan (whose voice is often featured on House of Cards) will also be performing with us.

The music we'll be singing tonight and Sunday is quite different from the music you hear on House of Cards, but you can really see his jazz background in some of the music, with extra crunchy chords and jazz-style improvisation.

Tickets are still available! If you're in the Chicagoland area, you can catch us tonight at the beautiful Fourth Presbyterian (across from the Hancock building) or Sunday at Alice Millar Chapel on Northwestern University's campus.

The Oatmeal and the Backfire Effect

Stop what you're doing and check out this great cartoon from the Oatmeal, dealing the backfire effect, a psychological phenomenon where information that is contrary to your beliefs actually strengthens your beliefs.

This concept is also sometimes called attitude polarization or belief polarization. Think of your attitude or belief as falling on a continuum, in terms of things like strength or importance - after all, most social psychologists do. Let's say you have an attitude that falls at the far right, close to the maximum. Information from the left might actually push you even farther right, up to the maximum (the poles).

If your attitude is a bit more wishy-washy (somewhere in the middle), it might not take much to move you to one side or the other. So backfire effects are strongest among people with strongly held attitudes or beliefs - generally the people who are more likely to act on those beliefs. We know that attitudes and behavior have a tenuous connection, but that connection is strongest when the attitude is specific and strong (a core belief).

On a side note: I wonder what it says about me that none of the "mind-blowing" facts presented in the cartoon ruffled my feathers. Either I'm really chill about hearing new information that might conflict with my beliefs or I'm feeling apathetic these days. (Yes.)

Mental Illness and Art

By now, you've probably at least heard of, if not watched, 13 Reasons Why, a series on Netflix that chronicles a set of tapes created by Hannah Baker to explain why she committed suicide. These tapes make it to our protagonist, Clay Jensen, a friend of Hannah's, and we learn about the events taking place prior to and after her suicide. It was a difficult series for me to watch - I had a cousin who committed suicide after his 30th birthday party, which coincidentally was also the day I graduated college. I awoke the next day to the news. A friend of mine who also experienced a family suicide mentioned that she was meaning to watch the show, and I passed on some trigger warnings for her. It's a difficult show to watch for anyone, but for people who have experienced firsthand the grief displayed by the characters of the show, especially Hannah's parents, it can bring back many conflicting emotions.

I do mean to sit down and write a review of the show. I think I need a little more distance, because I know I'm still feeling through many of emotions the show triggered. One thing the show has done is, it has started to get people talking about mental illness. In fact, that's what good art does - gets people thinking and talking about the human condition. In fact, two articles have crossed my path today, dealing with negative emotions more broadly and mental illness specifically.

The first is an interview with psychologist Susan David, whose book Emotional Agility deals with the importance of negative emotions, including in workplace settings. She argues that negative emotions should not be suppressed, because they can provide important information for the feeler and his/her coworkers:

A core part of emotional agility is the idea that our emotions are critical; they help us and our organizations. For example, if a person is upset that their idea was stolen at work, that’s a sign that they value fairness. Instead of being good or bad emotions, we should see emotions as containing useful data.

Our moods can provide us with important information - in fact, we refer to this in psychology as the "mood as information effect." If we realize we're in a negative mood, we analyze the situation to see what the cause could be. This mood is an indicator that something isn't right. Of course, Dr. David is going beyond mood as information and discussing how those emotions could inform others about what the person values. Further - and I'm sure she goes into this in her book - suppressing emotions can lead to thought suppression effects, where the suppressed emotions become stronger and more salient. The cognitive stress of suppressing would also make it more difficult for a person to do their job, especially jobs that require more critical thinking.

The other article deals with mental illness among artists, and asks whether pain is necessary to create great art:

Artists are masochists. We revel in the beauty of pain more than any other profession in the world. It's an experience we create for our viewers that is almost palpable. And it is in this experience that we connect to each other, creating everlasting bonds with our audience.

Some of the world's greatest artists have documented their own struggles with mental health. From depression and anxiety to a wide range of psychological disorders, these are all real themes that will always remain in art.

The article, written by artist Melody Nieves, who has struggled with depression herself, includes many great works of art, some familiar and some likely not, that deal with different aspects of mental illness and emotional pain: