Why deeply caring about the analysis isn’t always a good thing?

Illustration: a person looks at sheets of paper and thinks

Does Caring About the Analysis Matter?

The simplystatistics.org blog had an interesting discussion about podcast Roger Peng from simplystatistics.org recorded on A/B testing on Etsy. One of the late conclusions Roger Peng had is as follows
“Whether caring matters for data analysis also has implications for how to build a data analytic team. If you need your data analyst to be 100% committed to a product and to be fully invested, it’s difficult to achieve that with contractors or consultants, who are typically [not deeply invested].”

A hypothetical graph that show that $$ potential is lower as

Yes, deeply caring is very important. That is why I share Roger Peng’s skepticism about external contractors. On the other hand, too deep involvement is also a bad idea. Too deep involvement creates a bias. Such a bias, that can be conscious or subconscious, reduces critical thinking and increases the chances of false findings. If you don’t believe me, recall the last time you debugged a model after it produced satisfactory results. I bet you can’t. The reason is that we all tend to work hard, looking for errors and problems until we get the results we expect. But mostly, not long after that.

There are more mechanisms that may cause false findings. For a good review, I suggest reading  Why Most Published Research Findings Are False by John P. A. Ioannidis.
Image source: Data Analysis and Engagement – Does Caring About the Analysis Matter? — Simply Statistics

In defense of double-scale and double Y axes

Illustration: many graphs with secondary Y axes.

If you had a chance to talk to me about data visualization, you know that I dislike the use of double Y-axis for anything except for presenting different units of the same measurement (for example inches and meters). Of course, I’m far from being a special case.  Double axis ban is a standard stand among all the people in the field of data visualization education. Nevertheless, double-scale axes (mostly Y-axis) are commonly used both in popular and technical publications. One of my data visualization students in the Azrieli College of Engineering of Jerusalem told me that he continually uses double Y scales when he designs dashboards that are displayed on a tiny screen in a piece of sophisticated hardware. He claimed that it was impossible to split the data into separate graphs, due to space constraints, and that the engineers that consume those charts are professional enough to overcome the shortcomings of the double scales. I couldn’t find any counter-argument.

When I tried to clarify my position on that student’s problem, I found an interesting article by Financial Times commentator John Auther, called “Lies, Damned Lies and Statistics.” In this article, John Auther reviews the many problems a double scale can create. He also shows different alternatives (such as normalization). However, at the end of that article, John Auther also provides strong and valid arguments in favor of the moderate use of double scales. John Auther notices strange dynamics of two metrics

A chart with two Y axes - one for EURJPY exchange rate and the other for SPX Index
Screenshot from the article https://t.co/UYVqZpSzdS (Financial Times)

It is extraordinary that two measures with almost nothing in common with each other should move this closely together. In early 2007 I noticed how they were moving together, and ended up writing an entire book attempting to explain how this happened.

It is relatively easy to modify chart scales so that “two measures with almost nothing in common […] move […] closely together”. However, it is hard to argue with the fact that it was the double scale chart that triggered that spark in the commentator’s head.  He acknowledges that normalizing (or rebasing, as he puts it) would have resulted in a similar picture

Graph that depicts the dynamics of two metrics, brought to the same scale
Screenshot from the article https://t.co/UYVqZpSzdS (Financial Times)

But

However, there is one problem with rebasing, which is that it does not take account of the fact that a stock market is naturally more variable than a foreign exchange market. Eye-balling this chart quickly, the main message might be that the S&P was falling faster than the euro against the yen. The more important point was that the two were as correlated as ever. Both stock markets and foreign exchange carry trades were suffering grievous losses, and they were moving together — even if the S&P was moving a little faster.

I am not a financial expert, so I can’t find an easy alternative that will provide the insight John Auther is looking for while satisfying my purist desire to refrain from using double Y axes. The real question, however, is whether such an alternative is something one should be looking for. In many fields, double scales are the accepted language. Thanks to the standard language, many domain experts are able to exchange ideas and discover novel insights.  Reinventing the language might bring more harm than good. Thus, my current recommendations regarding double scales are:

Avoid double scales when possible, unless its a commonly accepted practice. In which case, be persistent and don’t lie.

 

Don’t take career advises from people who mistreat graphs this badly

Recently, I stumbled upon a report called “Understanding Today’s Chief Data Scientist” published by an HR company called Heidrick & Struggles. This document tries to draw a profile of the modern chief data scientist in today’s Big Data Era. This document contains the ugliest pieces of data visualization I have seen in my life. I can’t think of a more insulting graphical treatment of data. Publishing graph like these ones in a document that tries to discuss careers in data science is like writing a profile of a Pope candidate while accompanying it with pornographic pictures.

Before explaining my harsh attitude, let’s first ask an important question.

What is the purpose of graphs in a report?

There are only two valid reasons to include graphs in a report. The first reason is to provide a meaningful glimpse into the document. Before a person decided whether he or she wants to read a long document, they want to know what is it about, what were the methods used, and what the results are. The best way to engage the potential reader to provide them with a set of relevant graphs (a good abstract or introduction paragraph help too). The second reason to include graphs in a document is to provide details that cannot be effectively communicating by text-only means.

That’s it! Only two reasons. Sometimes, we might add an illustration or two, to decorate a long piece of text. Adding illustrations might be a valid decision provided that they do not compete with the data and it is obvious to any reader that an illustration is an illustration.

Let the horror begin!

The first graph in the H&S report stroke me with its absurdness.

Example of a bad chart. I have no idea what it means

At first glance, it looks like an overly-artistic doughnut chart. Then, you want to understand what you are looking at. “OK”, you say to yourself, “there were 100 employees who belonged to five categories. But what are those categories? Can someone tell me? Please? Maybe the report references this figure with more explanations? Nope.  Nothing. This is just a doughnut chart without a caption or a title. Without a meaning.

I continued reading.

Two more bad charts. The graphs are meaningless!

OK, so the H&S geniuses decided to hide the origin or their bar charts. Had they been students in a dataviz course I teach, I would have given them a zero. Ooookeeyy, it’s not a college assignment, as long as we can reconstruct the meaning from the numbers and the labels, we are good, right? I tried to do just that and failed. I tried to use the numbers in the text to help me filling the missing information and failed. All in all, these two graphs are a meaningless graphical junk, exactly like the first one.

The fourth graph gave me some hope.

Not an ideal pie chart but at least we can understand it

Sure, this graph will not get the “best dataviz” award, but at least I understand what I’m looking at. My hope was too early. The next graph was as nonsense as the first three ones.

Screenshot with an example of another nonsense graph

Finally, the report authors decided that it wasn’t enough to draw smartly looking color segments enclosed in a circle. They decided to add some cool looking lines. The authors remained faithful to their decision to not let any meaning into their graphical aidsScreenshot with an example of a nonsense chart.

Can’t we treat these graphs as illustrations?

Before co-founding the life-changing StackOverflow, Joel Spolsky was, among other things, an avid blogger. His blog, JoelOnSoftware, was the first blog I started following. Joel writes mostly about the programming business and. In order not to intimidate the readers with endless text blocks, Joel tends to break the text with illustrations. In many posts, Joel uses pictures of a cute Husky as an illustration. Since JoelOnSoftware isn’t a cynology blog, nobody gets confused by the sudden appearance of a Husky. Which is exactly what an illustration is – a graphical relief that doesn’t disturb. But what would happen if Joel decided to include a meaningless class diagram? Sure a class diagram may impress the readers. The readers will also want to understand it and its connection to the text. Once they fail, they will feel angry, and rightfully so

Two screenshots of Joel's blog. One with a Husky, another one with a meaningless diagram

The bottom line

The bottom line is that people have to respect the rules of the domain they are writing about. If they don’t, their opinion cannot be trusted. That is why you should not take any pieces of advice related to data (or science) from H&S. Don’t get me wrong. It’s OK not to know the “grammar” of all the possible business domains. I, for example, know nothing about photography or dancing; my English is far from being perfect. That is why, I don’t write about photography, dancing or creative writing. I write about data science and visualization. It doesn’t mean I know everything about these fields. However, I did study a lot before I decided I could write something without ridiculing myself. So should everyone.

 

What’s the most important thing about communicating uncertainty?

Screenshot: many images for the 2016 US elections

Sigrid Keydana, in her post Plus/minus what? Let’s talk about uncertainty (talk) — recurrent null, said

What’s the most important thing about communicating uncertainty? You’re doing it

Really?

Here, for example, a graph from a blog post

Thousands of randomly looking points. From https://myscholarlygoop.wordpress.com/2017/11/20/the-all-encompassing-figure/

The graph clearly “communicates” the uncertainty but does it really convey it? Would you consider the lines and their corresponding confidence intervals very uncertain had you not seen the points?

What if I tell you that there’s a 30% Chance of Rain Tomorrow? Will you know what it means? Will a person who doesn’t operate on numbers know what it means? The answer, to both these questions, is “no”, as is shown by Gigerenzer and his collaborators in a 2005 paper.

Screenshot: many images for the 2016 US elections

Communicating uncertainty is not a new problem. Until recently, the biggest “clients” of uncertainty communication research were the weather forecasters.  However, the recent “data era” introduced uncertainty to every aspect of our personal and professional lives. From credit risk to insurance premiums, from user classification to content recommendation, the uncertainty is everywhere. Simply “doing” uncertainty communication, as Sigrid Keydana from the Recurrent Null blog suggested isn’t enough. The huge public surprise caused by the 2016 US presidential election is the best evidence for that. Proper uncertainty communication is a complex topic. A good starting point to this complex topic is a paper Visualizing Uncertainty About the Future by David Spiegelhalter.

How to make a graph less readable? Rotate the text labels

This is my “because you can” rant.

Here, you can see a typical situation. You have some sales data that you want to represent using a bar plot.

01_default

Immediately, you notice a problem: the names on the X axis are not readable. One way to make the labels readable is to enlarge the graph.02_large_image

Making larger graphs isn’t always possible. So, the next default solution is to rotate the text labels.

03_rotated

However, there is a problem. Rotated text is read more slowly than standard horizontal text. Don’t believe me? This is not an opinion but rather a result of empirical studies [ref], [ref]. Sometimes, rotated text is unavoidable. Most of the time, it is not.

So, how do we make sure all the labels are readable without rotating them? One option is to move them up and down so that they don’t hinder each other. It is easily obtained with Python’s matplotlib

plt.bar(range(len(people)), sales)
plt.title('October sales')
plt.ylabel('$US', rotation=0, ha='right')
ticks_and_labels = plt.xticks(range(len(people)), people, rotation=0)
for i, label in enumerate(ticks_and_labels[1]):
    label.set_y(label.get_position()[1] - (i % 2) * 0.05)

(note, that I also rotated the Y axis label, for even more readability)

05_alternate_labels

Another approach that will work with even longer labels and that requires fewer code lines it to rotate the bars, not the labels.

07_horizontal_plot

… and if you don’t have a compelling reason for the data order, you might also consider sorting the bars. Doing so will not only make it prettier, it will also make it easier to compare between similar values. Use the graph above to tell whether Teresa Jackson’s sales were higher or lower than those of Marie Richardson’s. Now do the same comparison using the graph below.

08_horizontal_plot_sorted

To sum up: the fact you can does not mean you should. Sometimes, rotating text labels is the easiest solution. The additional effort needed to decipher the graph is the price your audience pays for your laziness. They might as well skip your graphs your message won’t stick.

This was my because you can rant.

Featured image by Flickr user gullevek

Good information + bad visualization = BAD

I went through my Machine Learning tag feed. Suddenly, I stumbled upon a pie chart that looked so terrible, I was sure the post would be about bad practices in data visualization. I was wrong. The chart was there to convey some information. The problem is that it is bad in so many ways. It is very hard to appreciate the information in a post that shows charts like that. Especially when the post talks about data science that relies so much on data visualization.

via Math required for machine learning — Youth Innovation

I would write a post about good practices in pie charts, but Robert Kosara, of https://eagereyes.org does this so well, I don’t really think I need to reinvent the weel. Pie charts are very powerful in conveying information. Make sure you use this tool well. I strongly suggest reading everything Robert Kosara has to say on this topic.

 

 

What are the best practices in planning & interpreting A/B tests?

Screenshots of the reading mentioned in this post

Compiled by my teammate Yanir Serourssi, the following is a reading list an A/B tests that you should read even if you don’t plan to perform an A/B test anytime soon. The list is Yanir’s. The reviews are mine. Collective intelligence in action 🙂

  • If you don’t pay attention, data can drive you off a cliff
    In this post, Yanir lists seven common mistakes that are common to any data-based analysis. At some point, you might think that this is a list of trivial truths. Maybe it is. The fact that Yanir’s points are trivial doesn’t make them less correct. Awareness doesn’t exist without knowledge. Unfortunately, knowledge doesn’t assure awareness. Which is why reading trivial truths is a good thing to do from time to time.
  • How to identify your marketing lies and start telling the truth
    This post was written by Tiberio Caetano, a data science professor at the University of Sidney. If I had to summarize this post with a single phrase, that would be “confounding factors”. A confounding variable is a variable hidden from your eye that influences a measured effect. One example of a confounding variable is when you start an ad campaign for ice cream, your sales go up, and you conclude that the ad campaign was effective. What you forgot was that the ad campaign started at the beginning of the summer, when people start buying more ice cream anyhow.
    See this link for a detailed textbook-quality review of confounding variables.
  • Seven rules of thumb for web site experimenters
    I read this review back in 2014, shortly after it was published by, among others, researchers from Microsoft and LinkedIn. Judging by the title, one would expect yet another list of trivial truths in a self-promoting product blog. This is not the case here. In this paper, you will find several real-life case studies, many references to marketing studies, and no advertising of shady products or schemes.
  • A dirty dozen: Twelve common metric interpretation pitfalls in online controlled experiments
    Another academic paper by Microsoft researchers. This one lists a lot of “dont’s”. Like in the previous link, every advice the authors give is based on established theory and backed up by real data.