Big Data, Plainly Spoken (aka Numbers Rule Your World)

Vaping saves lives. Maybe.

My new Inside the Black Box video walks through how one evaluates the claim by Juul and e-cigarette companies that e-cigarettes will save lives.

This claim is substantively similar to Tesla's claim that its "autopilot" technology saves lives. See my previous video for a discussion of Tesla's claim.

Although stated absolutely, these technology vendors are in fact making a relative claim. Tesla argues that human drivers are more prone to accident fatalities while e-cigarette companies use smokers of tobacco as the strawman. In both cases, the question is whether they are making the right comparisons.

For the vaping analysis, I look at eight subgroups of people to see how they are affected. See the video for details. The following slide is a summary:

 

The credibility of the claim depends on (a) do smokers completely switch out of tobacco? (b) how many non-smokers pick up vaping? (c) are there harms from vaping as yet unknown? [pending results of the ongoing investigation]

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Bonus for those wanting to dig in deeper:

The analysis sounds like a pre-post analysis of behavorial change across time surrounding an event of interest (the launch of vaping). While this interpretation is fine, it is limited because it's a point in time.

There is a better way of thinking about what we've done.

Given that we are now living in the "post-vaping" world, the "pre-vaping" world is a counterfactual. We are imagining how people would have behaved as if e-cigarettes did not exist. We observe the post-vaping world in which we live, and imagine the pre-vaping world we don't.

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Tesla's slick stats

Tesla likes to advertise that its cars save lives. In the latest video, I examine this claim. The problem is that Tesla owners should not be compared to the average car buyer. Tesla is a luxury car and a status symbol. Tesla should be compared to other luxury cars. How does Tesla fare? Check out the video to find out.

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One additional issue: what's the better metric for automotive fatality rate? Tesla uses deaths per million vehicle miles driven while others in industry use deaths per million vehicle-years.

This reminds me of the same issue in measuring air safety, a topic I addressed in my first book, Numbers Rule Your World (Chap 5) (link). Should airlines be measured based on deaths per million flight miles or deaths per million flights? MIT Professor Arnold Barnett looked into this issue and came out advocating for deaths per million flights. The main insight is that most air crashes happen at the start or end of a flight; the probability of a crash is not uniformly distributed over the miles of each journey. Thus, the death per million flights metric gets closer to the risk experience.

I'd imagine something similar at work here. The chance of getting killed in a car accident is not spread evenly throughout a trip. The additional risk is borne by taking an additional trip - once you're on a trip, the increase in risk over the distance of the trip isn't material.

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To see the short video on Tesla, click here.

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The list of worst college majors, and why you should ignore it

Business Insider reports on the bottom 20 college majors, with the warning "what not to study". The list looks extremely random including everything from visual and performing arts to international relations to math and computer science (!).

The author explains these majors have the highest unemployment rates.

Here are a few reasons why you can and should ignore this study.

1. Presumably the journalist is advising college students, who will soon become new college graduates. But the analysis references college graduates which include anyone with a college degree, ranging from a new college graduate to someone who's worked for 40 years. New college graduates have a much higher unemployment rate than all college graduates. (See this recent Huffington Post article.)
   
   
2. Students do not randomly select a college major. Thus, students who choose to major in international relations are not the same type of people who decide to major in engineering. If an international relations student were to switch major to engineering, it would not follow that this person's employment prospect had suddenly brightened. It might even worsen because (a) the student could be relatively less competitive against other engineering majors; and (b) the student and the major might now be mismatched.
   
   
3. If everyone followed this journalist's advice, then each college would need a very small number of majors. Unless the economy suddenly produced a bonanza of jobs, where would the unemployed go? You got it... the unemployment rate would accrue to the several remaining majors, instead of being spread out over hundreds of majors. The unemployment rate of these "good" majors would rise to the level of the average unemployment rate. Oops.
   
   
4. Not everyone needs or wants a job. Changing majors doesn't change that reality.

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There is an even bigger howler in the article. The analyst at Bankrate equated "job stability" with low unemployment rate, using language such as "Having a high-paying job doesn't necessarily mean you'll have job stability, and vice versa." So, majors with high unemployment rates are bad because people job-hop.

But the unemployment rate is not a measure of job tenure, and thus not an indicator of job stability. In fact, if people job-hop a lot, the unemployment rate will be relatively low, because the same job can be held by multiple people in the course of a year.

Let's imagine a country of 10 people with 5 jobs. The unemployment rate is 50%. If no one changes jobs, the same five people are employed always, and the rate is stable at 50%. The government stipulates that no person can hold on to the same job for longer than 6 months. We put the 10 people in a circle, and every other person is given a chair and seated. Every 6 months, each person moves clockwise by one slot: the five previously seated no longer have a seat, the five seats now occupied by the five previously standing. Everyone is now employed 6 months out of the year. The employment rate is now 100% (counting part-timers).

Thus, unemployment rate of zero can coincide with high job instability.

 

 

Data Science: The Missing Pieces - Functions

In a new series of videos, I plan to cover the "missing pieces" of data science. These are the little things that instructors assume students already know. These little things block further learning if not cleared up.

 

The first video is about functions. Anyone learning to code must know about functions. What's covered?

• What is a function?
• Why use functions?
• What are built-in functions?
• What is a package?
• What are arguments and parameters?
• What are default values?
• How to call a function?
• How to call a nested function?
• How to define your own function?
• and more!

The video explains all the important bits in 13 minutes.

I'm taking suggestions for other "missing pieces".

 

 

Practical advice for data science career success

My head was nodding so much I was worried about getting a neckache while reading this article by Adam Kovarik in Amstat News (July 2019).

He titled the piece "Beyond the Technical: Connecting Data Skills to Business Skills." Years ago, for one of my first keynote speeches, I used something similar: "What Happens After the Math is Done?"

Here are the key points (all quotes from Kovarik):

• [Your audience] do not want nor do they need the nominal difference in AICc for the three models you ran...they have decisions to make and a bottom line to impact.
• Focusing on the minutia may sell your intellect, but it may also disconnect you from the true goals of the organization. Once this happens, your analysis becomes a footnote.
• You spent eight hours putting together an analysis on revenue impact. Now spend eight hours validating your outcome.
• You will be unambiguously judged by the accuracy of your figures... this means you must unconditionally adopt a routine inclusive of quality assurance.
• The swiftest way to lose your credibility is to overlook an obviously noticeable data error... if your senior vice president is the one to spot that the number of customers in your analysis only added up to 50% of the actual, do not expect him/her to accept your conclusion so readily and resolutely. [I'd add: s/he may distrust not just this one analysis but every analysis you'll put out from that point on. There is no "forget" button you can press.]
• Do not detail your journey when you are at the table... the more background you present, the more opportunity there will be for individuals to take the discussion down a rabbit hole.
• Listen... write down what you hear.
• [Don't] be the individual who starts solving the problem in his/her head in the midst of the meeting.
• the sooner you recognize the tools are a means to an end, the sooner you will focus more on the "end" and less on the "tool".
• Keep learning.

Unfortunately, some of the things you learned in college are precisely the things that will get you in trouble. Explaining things from first principles, describing the journey (the wrong turns, the obstacles, etc.), holding the conclusion as cliff-hanger, focusing on technical outcomes only, believing that arriving at the wrong number is okay so long as you used the right methodology, being obsessed with edge cases rather than the most probable case, etc. etc.

I am not saying your teachers were wrong. But to have success in an industry career, you need to adapt.

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