Book review. The War of Art by S. Pressfield

TL;DR: This is a long motivational book that is “too spiritual” for the cynic materialist that I am.

The War of Art by [Pressfield, Steven]

The War of Art is a strange book. I read it because “everybody” recommended it. This is what Derek Sivers’ book recommendation page says about this book

Have you experienced a vision of the person you might become, the work you could accomplish, the realized being you were meant to be? Are you a writer who doesn’t write, a painter who doesn’t paint, an entrepreneur who never starts a venture? Then you know what “Resistance” is.

As a known procrastinator, I was intrigued and started reading. In the beginning, the book was pretty promising. The first (and, I think, the biggest) part of the book is about “Resistance” — the force behind the procrastination. I immediately noticed that almost every sentence in this chapter could serve a motivational poster. For example

  • It’s not the writing part that’s hard. What’s hard is sitting down to write.
  • The danger is greatest when the finish line is in sight.
  • The most pernicious aspect of procrastination is that it can become a habit.
  • The more scared we are of a work or calling, the more sure we can be that we have to do it.

Individually, each sentence makes sense, but their concentration was a bit too much for me. The way Pressfield talks about Resistance resembles the way Jewish preachers talk about Yetzer Hara: it sits everywhere, waiting for you to fail. I’ tdon’t like this approach.

The next chapters were even harder for me to digest. Pressfield started talking about Muses, gods, prayers, and other “spiritual” stuff; I almost gave up. But I fought the Resistance and finished the book.

My main takeaways:

  • Resistance is real
  • It’s a problem
  • The more critical the task is, the stronger is the Resistance. OK, I kind of agree with this. Pressfield continues to something do not agree with: thus (according to the author), we can measure the importance of a task by the Resistance it creates.
  • Justifying not pursuing a task by commitments to the family, job, etc. is a form of Resistance.
  • The Pro does stuff.
  • The Artist is a Pro (see above) who does stuff even if nobody cares.

Data visualization with statistical reasoning: seeing uncertainty with the bootstrap — Dataviz – Stats – Bayes

On Sunday, I wrote about bootstrapping. On Monday, I wrote about visualization uncertainty. Let’s now talk about bootstrapping and uncertainty visualization.

Robert Grant is a data visualization expert who wrote a book about interactive data visualization (which I should read, BTW).

Robert runs an interesting blog from which I learned another approach to uncertainty visualization, bootstrapping.

Source: Robert Grant.

Read the entire post: Data visualization with statistical reasoning: seeing uncertainty with the bootstrap — Dataviz – Stats – Bayes

On MOOCs

When Massive Online Open Courses (a.k.a MOOCs) emerged some X years ago, I was ecstatic. I was sure that MOOCs were the Big Boom of higher education. Unfortunately, the MOOC impact turned out to be very modest. This modest impact, combined with the high production cost was one of the reasons I quit making my online course after producing two or three lectures. Nevertheless, I don’t think MOOCs are dead yet. Following are some links I recently read that provide interesting insights to MOOC production and consumption.

  • A systematic study of academic engagement in MOOCs that is scheduled for publication in the November issue of Erudit.org. This 20+ page-long survey summarizes everything we know about MOOCs today (I have to admit, I only skimmed through this paper, I didn’t read all of it)
  • A Science Magazine article from January, 2019. The article, “The MOOC pivot,” sheds light to the very low retention numbers in MOOCs.
  • On MOOCs and video lectures. Prof. Loren Barbara from George Washington University explains why her MOOCs are not built for video. If you consider creating an online class, you should read this.
  • The economic consequences of MOOCs. A concise summary of a 2018 study that suggest that MOOC’s economic impact is high despite the high churn rates.
  • Thinkful.com, an online platform that provides personalized training to aspiring data professionals, got in the news three weeks ago after being purchased for $80 million. Thinkful isn’t a MOOC per-se but I have a special relationship with it: a couple of years ago I was accepted as a mentor at Thinkful but couldn’t find time to actually mentor anyone.

The bottom line

We still don’t know how this future will look like and how MOOCs will interplay with the legacy education system but I’m sure the MOOCs are the future

Error bars in bar charts. You probably shouldn’t

This is another post in the series Because You Can. This time, I will claim that the fact that you can put error bars on a bar chart doesn’t mean you should.

It started with a paper by prof. Gerd Gigerenzer whose work in promoting numeracy I adore. The paper, “Natural frequencies improve Bayesian reasoning in simple and complex inference tasks” contained a simple graph that meant to convince the reader that natural frequencies lead to more accurate understanding (read the paper, it explains these terms). The error bars in the graph mean to convey uncertainty. However, the data visualization selection that Gigerenzer and his team selected is simply wrong.

First of all, look at the leftmost bar, it demonstrates so many problems with error bars in general, and in error bars in barplots in particular. Can you see how the error bar crosses the X-axis, implying that Task 1 might have resulted in negative percentage of correct inferences?

The irony is that Prof. Gigerenzer is a worldwide expert in communicating uncertainty. I read his book “Calculated risk” from cover to cover. Twice.

Why is this important?

Communicating uncertainty is super important. Take a look at this 2018 study with the self-explaining title “Uncertainty Visualization Influences how Humans Aggregate Discrepant Information.” From the paper: “Our study repeatedly presented two [GPS] sensor measurements with varying degrees of inconsistency to participants who indicated their best guess of the “true” value. We found that uncertainty information improves users’ estimates, especially if sensors differ largely in their associated variability”.

Image result for clinton trump polls
Source HuffPost

Also recall the surprise when Donald Trump won the presidential elections despite the fact that most of the polls predicted that Hillary Clinton had higher chances to win. Nobody cared about uncertainty, everyone saw the graphs!

Why not error bars?

Keep in mind that error bars are considered harmful, and I have a reference to support this claim. But why?

First of all, error bars tend to be symmetric (although they don’t have to) which might lead to the situation that we saw in the first example above: implying illegal values.

Secondly, error bars are “rigid”, implying that there is a certain hard threshold. Sometimes the threshold indeed exists, for example a threshold of H0 rejection. But most of the time, it doesn’t.

stacked round gold-colored coins on white surface

More specifically to bar plots, error lines break the bar analogy and are hard to read. First, let me explain the “bar analogy” part.

The thing with bar charts is that they are meant to represent physical bars. A physical bar doesn’t have soft edges and adding error lines simply breaks the visual analogy.

Another problem is that the upper part of the error line is more visible to the eye than the lower one, the one that is seen inside the physical bar. See?undefined

But that’s not all. The width of the error bars separates the error lines and makes the comparison even harder. Compare the readability of error lines in the two examples below

The proximity of the error lines in the second example (take from this site) makes the comparison easier.

Are there better alternatives?

Yes. First, I recommend reading the “Error bars considered harmful” paper that I already mentioned above. It not only explains why, but also surveys several alternatives

Nathan Yau from flowingdata.com had an extensive post about different ways to visualize uncertainty. He reviewed ranges, shades, rectangles, spaghetti charts and more.

Claus Wilke’s book “Fundamentals of Data Visualization” has a dedicated chapter to uncertainty with and even more detailed review [link].

Visualize uncertainty about the future” is a Science article that deals specifically with forecasts

Robert Kosara from Tableu experimented with visualizing uncertainty in parallel coordinates.

There are many more examples and experiments, but I think that I will stop right now.

The bottom line

Communicating uncertainty is important.

Know your tools.

Try avoiding error bars.

Bars and bars don’t combine well, therefore, try harder avoiding error bars in bar charts.

You don’t need a fast way to increase your reading speed by 25%. Or, don’t suppress subvocalization

Not long ago, I wrote a post about a fast hack that increased my reading speed by tracking the reading with a finger. I think that the logic behind using a tracking finger is to suppress subvocalization. I noticed that, at least in my case, suppressing subvocalization reduces the fun of reading. I actually enjoy hearing the inner voice that reads the book “with me”.

Bootstrapping the right way?

Many years ago, I terribly overfit a model which caused losses of a lot of shekels (a LOT). It’s not that I wasn’t aware of the potential overfitting. I was. Among other things, I used several bootstrapping simulations. It turns out that I applied the bootstrapping in a wrong way. My particular problem was that I “forgot” about confounding parameters and that I “forgot” that peeping into the future is a bad thing.

Anyhow, Yanir Seroussi, my coworker data scientist, gave a very good talk on bootstrapping.

Yanir Seroussi

Bootstrapping the right way is a talk I gave earlier this year at the YOW! Data conference in Sydney. You can now watch the video of the talk and have a look through the slides. The content of the talk is similar to a post I published on bootstrapping pitfalls, with some additional simulations.

The main takeaways shared in the talk are:

  • Don’t compare single-sample confidence intervals by eye
  • Use enough resamples (15K?)
  • Use a solid bootstrapping package (e.g., Python ARCH)
  • Use the right bootstrap for the job
  • Consider going parametric Bayesian
  • Test all the things

Testing all the things typically requires writing code, which I did for the talk. You can browse through it in this notebook. The most interesting findings from my tests are summarised by the following figure.

Revenue confidence intervals

The figure shows how the accuracy of confidence interval estimation varies by algorithm, sample size…

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Visualizations with perceptual free-rides

Dr. Richard Brath is a data visualization expert who also blogs from time to time. Each post in Richard’s blog provides a deep, and often unexpected to me, insight into one dataviz aspect or another.

richardbrath

We create visualizations to aid viewers in making visual inferences. Different visualizations are suited to different inferences. Some visualizations offer more additional perceptual inferences over comparable visualizations. That is, the specific configuration enables additional inferences to be observed directly, without additional cognitive load. (e.g. see Gem Stapleton et al, Effective Representation of Information: Generalizing Free Rides2016).

Here’s an example from 1940, a bar chart where both bar length and width indicate data:

Walter_Weld__How_to_chart_data_1960_hathitrust2

The length of the bar (horizontally) is the percent increase in income in each industry.  Manufacturing has the biggest increase in income (18%), Contract Construction is second at 13%.

The width of the bar (vertically) is the relative size of that industry: Manufacturing is wide – it’s the biggest industry – it accounts for about 23% of all industry. Contract Construction is narrow, perhaps the third smallest industry, perhaps around 3-4%.

What’s really interesting is that

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Book review. Indistractable by Nir Eyal

Nir Eyal is known for his book “Hooked” in which he teaches how to create addictive products. In his new book “Indistractable“, Nir teaches how to live in the world full of addictive products. The book itself isn’t bad. It provides interesting information and, more importantly, practical tips and action items. Nir covers topics such as digital distraction, productivity and procrastination.

Indistractable Control Your Attention Choose Your Life Nir Eyal 3D cover

I liked the fact that the author “gives permission” to spend time on Facebook, Instagram, Youtube etc, as long as it is what you planned to do. Paraphrasing Nir, distraction isn’t distraction unless you know what it distracts you from. In other words, anything you do is a potential distraction unless you know what, why and when you are doing it.

My biggest problem with this book is that I already knew almost everything that Nir wrote. Maybe I already read too many similar books and articles, maybe I’m just that smart (not really) but for me, most of Indistractable wasn’t valuable.

Until I got to the chapter that deals with raising children (“Part 6, how to raise indistractable children”). I have to admit, when it comes to speaking about raising kids in the digital era, Nir is a refreshing voice. He doesn’t join the global hysteria of “the screens make zombies of our kids”. Moreover, Nir brings a nice collection of hysterical prophecies from the 15th, 18th and 20th centuries in which “experts” warned about the bad influence new inventions (such as printed books, affordable education, radio) had on the kids.

Displaying Photo note

Another nice touch is the fact that each chapter has a short summary that consists of three-four bullet points. Even nicer is the fact that Nir copied all the “Remember this” lists at the end of the book, which is very kind of him.

The Bottom line. 4/5. Read.

14-days-work-month — The joys of the Hebrew calendar

Tishrei is the seventh month of the Hebrew calendar that starts with Rosh-HaShana — the Hebrew New Year*. It is a 30 days month that usually occurs in September-October. One interesting feature of Tishrei is the fact that it is full of holidays: Rosh-HaShana (New Year), Yom Kippur (Day of Atonement), first and last days of Sukkot (Feast of Tabernacles) **. All these days are rest days in Israel. Every holiday eve is also a de facto rest day in many industries (high tech included). So now we have 8 resting days that add to the usual Friday/Saturday pairs, resulting in very sparse work weeks. But that’s not all: the period between the first and the last Sukkot days are mostly considered as half working days. Also, the children are at home since all the schools and kindergartens are on vacation so we will treat those days as half working days in the following analysis.

I have counted the number of business days during this 31-day period (one day before the New Year plus the entire month of Tishrei) between 2008 and 2023 CE, and this is what we get:

Overall, this period consists of between 15 to 17 non-working days in a single month (31 days, mind you). This is how the working/not-working time during this month looks like this:

Now, having some vacation is nice, but this month is absolutely crazy. There is not a single full working week during this month. It is very similar to the constantly interrupt work day, but at a different scale.

So, next time you wonder why your Israeli colleague, customer or partner barely works during September-October, recall this post.

(*) New Year starts in the seventh’s month? I know this is confusing. That’s because we number Nissan — the month of the Exodus from Egypt as the first month.
(**)If you are an observing Jew, you should add to this list Fast of Gedalia, but we will omit it from this discussion