Search This Blog

Monday, October 7, 2019

Puppies, Mandopop, and Causality

Here's the best puppy video that I've ever seen on YouTube. It's only 9 seconds. Trust me, it's worth watching.


It's even better looped. Try it with YouTube's loop feature. It's just gets better and better.

After I watched it a few times, I wondered what that catchy song was. It didn't take long for Shazam to identify it, but I struggled a bit getting the Chinese characters into a search field. I still don't know the name of the song, but it's recorded by the Mandopop* group By2.


Here are some comments for the song's video.



There's a clear causal claim here: the popularity of the puppy video caused the popularity of the song video. 

That sounds reasonable, but...    ... is it true?

Think about why the puppy video is so great. Sure, the rear dog's face plant into the snow is funny and the can-do spirit he shows by quickly jumping back up is inspirational. But I think it's the way the song perfectly syncs to the front dog's bounces that makes the video great.

At first, you think that the dog syncs to the song but that's not true. The song was added later. The dog does not sync to the song. The song syncs to the dog. That's an important distinction.

Compare the first version of the puppies to this one. It's longer, about a minute, but you need to watch only the first 10 to 20 seconds.


It's the same puppies doing the same thing but it's not nearly as cute. 

That should make you think about the causal claim in the music video's comments. Maybe the puppy video is not the cause of the song's popularity. Maybe they have it backwards. Maybe, just maybe, the song is the cause of the puppy video's popularity. 

Then again, maybe the comments are right. Either way, you can't confirm a causal relationship from a simple observational relationship**.

Unjustified causal conclusions are a common statistical error. When two variables show a relationship and "it makes sense" that A causes B***, we often jump to a causal conclusion without question. The jump to a causal conclusion is so common that I think we'd benefit from training ourselves to think the other way. Our first reaction to a statistical relationship should be to question any causal relationship. 

--------
* Thanks to my colleague Chao Zheng for clarifying that the song is Mandarin instead of Cantonese.

** To be clear. I don't think the presence of two different sound tracks on the same puppy video is sufficient to claim there's an experiment. This is still observational. However, it's a good thought exercise to think about how you would make it an experiment.

*** Or, worse yet, we already believed the causal relationship and we were just looking for data to prove ourselves right.

Thursday, September 5, 2019

Qualitative Data is Data Too (Part 3)

I laid out the value of qualitative data in Part 1 and and gave an example in Part 2. In this part, I've got my own analysis. Here's a series of qualitative data points.

Once upon a time, Council Bluffs Iowa was a major stopping point for westward travelers due to the presence of a steam powered boat to ferry wagons and livestock over the Missouri River. Then someone decided that a transcontinental railroad would be a good idea. Wagon trains were fine, but railroads would be faster and more efficient for people, products, and livestock.

The big question: where would it go?

Several broad routes were considered and a "central" route was selected. Within the central route, an eastern starting point had to be selected. As POTUS, Abraham Lincoln would make the decision.

Just imagine the economic loss for Council Bluffs if Kansas City Missouri were selected as the starting point. Council Bluffs would have turned into a ghost town.

As luck would have it, one of the investors had previously employed Lincoln as an attorney and this investor wanted Council Bluffs selected. There's no way to be certain, but it would be reasonable to conclude that his connection to Lincoln influenced the decision. Council Bluffs was selected and by the 1930's its status as a wagon train stopping point was replaced by its status as the 5th largest rail center in the US.

The invention of the automobile again changed the nature of transportation and Route 66 was established in 1926. When completed it ran from Chicago to Santa Monica and became one of the most famous highways in US history. By 1930, trucks rivaled rail for dominance in shipping.

Route 66 was a financial boon for towns and businesses along the route but, like many highways, its path wasn't static.

In Atlanta Illinois, the original route ran right through town and businesses thrived. Twenty years later, a bypass was built and businesses died.

In New Mexico, an angry Governor used his lame-duck power to move Route 66 and bypass the state capital: "In 1924, Democrat Arthur Thomas Hannett was unexpectedly elected for a single term (1925–1927) as governor, only to be defeated with various dirty tricks in the next election. Blaming the Republican establishment in Santa Fe for his defeat, Hannett used the lame duck remainder of his term to force through a sixty-nine mile cutoff from Santa Rosa directly to Albuquerque, bypassing Santa Fe entirely."

These are just a few of many examples of Route 66 changes over the years. Most were not as blatantly political as in New Mexico, but each change still had politics in the background. Whether they were elected or appointed, someone or some group made the decision. 

Just as rail usurped wagons and highways usurped rail, interstates began usurping highways in the 1950s. Portions of these interstates (I55, I44, I40) follow Route 66 very closely but most of Route 66 was bypassed. Much of the current Route 66 nostalgia focuses on the economic impact of the interstates and how they created a new generation of ghost towns. 

This might not look like "data". Maybe you think it's just a story, a narrative of changing modes of transportation (admittedly a very abbreviated narrative). However, I would argue that there's a discernible pattern here and that we can derive insights from that pattern.

1) Things change. Downtown dime stores (Kresge's, Ben Franklin, Grants) had a little of everything and threatened their neighboring, specialized stores. Then Kresge's morphed in big-box discount K-Mart and threatened all of downtown. Walmart came along and knocked K-mart off the top of the hill. The internet came along and Amazon knocked Walmart off the top. 

2) When things change, politics matters. This is actually the main point, but I used #1 to emphasize the inevitability of change. Changes in transportation required political decisions about routes and right-of-ways. Changes in retail required political decisions about zoning, building codes, and taxation. If you plan to be in business, then you need to understand politics. Study politics. Study political economy. Read Travels of a T-shirt and pay attention to the policy and regulatory decisions made by a myriad of political bodies. 

3) Nostalgia is selective. This is a minor point, but I still find it interesting. Route 66 is "hot" at the moment. Perhaps it's because of Disney's Cars or maybe it's relative to age of the Baby Boomers. Either way, there are books, blogs, articles, etc. about the sad loss of prosperity on Route 66. I don't see this level of concern over the ghost towns created when wagon trains disappeared. More recently, Lena Wisconsin's downtown was bypassed by reconstruction of Highway 141. Maybe the locals talk about the impact on Lena's downtown, but there's no national discussion that I've ever heard about.

Sources:
  • https://en.wikipedia.org/wiki/U.S._Route_66
  • https://www.national66.org/history-of-route-66/
  • http://library.nau.edu/speccoll/exhibits/route66/paths.html
  • https://en.wikipedia.org/wiki/U.S._Route_66_in_New_Mexico
  • https://www.route66news.com/2007/03/19/road-to-albuquerque-was-a-joke/
  • Personal experience gathered on a recent Route 66 vacation


Millenials Again - They Still Aren't All That Different

Last year, I wrote a post about millennials, demographics, and the risk of making overly broad generalizations.

Here's another report telling us that millenials are't all that different than previous generations. They might be hitting some "life moments" later, but they still want to the same things that their parents wanted.

Friday, August 23, 2019

Qualitative Data is Data Too (Part 2)

In Part 1, I contrasted traditional data analysis and qualitative analysis. In this part, I tell a story that combines them.

Unfortunately, I don't have a source for the story. At one point, I thought I read it in the work of Russ Ackoff but I haven't been able to find it in his writing. Then I thought it might be from Gene Woolsey. I was fortunate enough to spend some time on the phone with Dr. Woolsey toward the end of his career. He agreed that the story sounded like something that either he or Ackoff would have written but it wasn't his and he didn't recognize it from Ackoff's work. Therefore, even though I know that the story exists, I can't cite it and it remains apocryphal.

Since I can't find the source, I can't double-check the details. If anyone can confirm a source, please let me know.

Here's the story...

Back around 1970, a quantitative analyst was hired as a consultant by a Fortune 500 firm in Los Angeles. They were considering moving their operation out of the city to the suburbs and they wanted him to evaluate the long-range cost/savings of expanding where they were versus relocating. The consultant gathered the necessary data, did the analysis, and concluded that there would be significant savings if the company moved. He reported his results, got paid for his time, and went on his way. Nothing changed at the company.

About 18 months later, he ran into the CEO who had hired him. He brought up the project and apologized that his work hadn't been useful. The CEO seemed surprised and assured him that his analysis had been crucial to their decision.

"But you're not moving" the consultant said.

"No, we're not" admitted the CEO. "We wanted to stay in the city. We thought it was better for our employees and we believed that we had a positive impact on our neighborhood. Our corporate mission seemed to fit with the city location. There was a lot of pressure to move and save money but we didn't know what the savings would be or, conversely, what the cost would be to stay put. Your analysis made the numbers clear and we decided that would afford to not move."

The moral of the story? The analyst did his job with a traditional quantitative analysis. The company made their decision on a combination of quantitative and qualitative factors. On the numbers alone, one could argue that the company reached the wrong conclusion. On the qualitative analysis, it wasn't clearly a right or wrong conclusion but it was a decision that the executives and the board were comfortable with.

In Part 3, I'll give an example of my own qualitative analysis. By necessity, my conclusions will be "fuzzy" and you might disagree with them but you should still be able to follow the logic connecting my observations and conclusions.