This chart advises webpages to add more words

A reader sent me the following chart. In addition to the graphical glitch, I was asked about the study's methodology.


I was able to trace the study back to this page. The study uses a line chart instead of the bar chart with axis not starting at zero. The line shows that web pages ranked higher by Google on the first page tend to have more words, i.e. longer content may help with Google ranking.


On the bar chart, Position 1 is more than 6 times as big as Position 10, if one compares the bar areas. But it's really only 20% larger in the data.

In this case, even the line chart is misleading. If we extend the Google Position to 20, the line would quickly dip below the horizontal axis if the same trend applies.

The line chart includes too much grid, one of Tufte's favorite complaints. The Google position is an integer and yet the chart's gridlines imply that 0.5 rank is possible.

Any chart of this data should supply information about the variance around these average word counts. Would like to see a side-by-side box plot, for example.

Another piece of context is the word counts for results on the second or third pages of Google results. Where are the short pages?


Turning to methodology, we learn that the research team analyzed 1 million pages of Google search results, and they also "removed outliers from our data (pages that contained fewer than 51 words and more than 9999 words)."

When you read a line like this, you have to ask some questions:

How do they define "outlier"? Why do they choose 51 and 9,999 as the cut-offs?

What proportion of the data was removed at either end of the distribution?

If these proportions are small, then the outliers are not going to affect that average word count by much, and thus there is no point to their removal. If they are large, we'd like to see what impact removing them might have.

In any case, the median is a better number to use here, or just show us the distribution, not just the average number.

It could well be true that Google's algorithm favors longer content, but we need to see more of the data to judge.



Speed demon quartered and shrunk

Reader Richard K. submitted a link to Microsoft Edge's website.

Screen Shot 2017-08-09 at 10.00.08 PM

This chart uses three speedometers to tell the story that Microsoft's Edge browser is faster than Chrome or Firefox. These speedometer charts are disguised racetrack charts. Read last week's post first if you haven't.

Richard complained the visual design distorting the data. How the distortion entered the picture is a long story. Let's begin with an accurate representation of the data:


Next, we pull those speedometer curves straight:


While the three values are within 10 percent of each other, the lengths of the two shorter curves are only 40-50 percent of the length of the longest one! This massive distortion is due to not starting the axis (i.e., speedometer) at zero.

We now put the missing 25,000 back onto the chart, proportionally expanding each bar. As seen below, fixing the axis does not get us back to the desired relative lengths, so some other distorting factor is at play.


The culprit is that the middle speedometer is 44 percent larger than the other two. If we inflate the side bars by 44 percent, the world is made right again. Phew!





If Clinton and Trump go to dinner, do they sit face to face, or side by side?

One of my students tipped me to an August article in the Economist, published when last the media proclaimed Donald Trump's campaign in deep water. The headline said "Donald Trump's Media Advantage Falters."

Who would have known, judging from the chart that accompanies the article?


There is something very confusing about the red line, showing "Trump August 2015 = 1." The data are disaggregated by media channel, and yet the index is hitched to the total of all channels. It is also impossible to figure out how Clinton is doing relative to Trump in each channel.

Here is a small-multiples rendering that highlights the key comparisons:


Alternatively, one can plot the Clinton advantage versus Trump in each channel, like this:


One sees that Clinton has caught up in the last month (July 2016), primarily through more coverage by "online news."

Imagine Mr. Trump and Mrs. Clinton dining at a restaurant. Are they seated side by side (Economist) or face to face (junkcharts)?

After seeing this chart, my mouth needed a rinse

The credit for today's headline goes to Andrew Gelman, who said something like that when I presented the following chart at his Statistical Graphics class yesterday:

Fidelityad_consumerstaples_adj_smWith this chart (which appeared in a large ad in the NY Times), Fidelity Investment wants to tell potential customers to move money into the consumer staples category because of "greater return" and "lower risk". You just might wonder what a "consumer staple" is. Toothbrushes, you see.

There are too many issues with the chart to fit into one blog post. My biggest problem concerns the visual trickery used to illustrate "greater" and "lower". The designer wants to focus readers on the two orange brushes: return for consumer staples is higher, and risk is lower, you see.

The "greater" (i.e. right-facing) toothbrush is associated with longer brushes and higher elevation; the "lower" (left-facing) toothbrush, with shorter brushes and lower elevation.

But looking carefully at the scales reveals that the return ranges from 6% to 14% and the risk ranges from 10% to 25%. So larger numbers are depicted by shorter brushes and lower elevation, exactly the opposite of one's expectation. The orange brushes happen to  represent the same value of 14.3% but the one on the right is at least four times as large as the one on the left. As the dentist says, time to rinse out!

The vertical axis represents ranking of the investment categories in terms of decreasing return and/or risk so on both toothbrushes, the axis should run from 1 to 10.


How would the dentist fix this?

The first step is to visit the Q corner of the Trifecta Checkup. The purpose of this chart is for investors to realize that (using the chosen metrics) consumer durables have the best combination of risk and return. In finance, risk is measured as the volatility of return. So, in effect, all the investors care about is the probability of getting a certain level of return.

The trouble with any chart that shows both risk and return is that readers have no way of going from the pair of numbers to the probability of getting a certain level of return.

The fix is to plot the probability of returns directly.


In the above sketch, I just assumed a normal probability model, which is incorrect; but it is not hard to substitute this with an empirial distribution, if you obtain the raw data.

Unlike the original chart, it does not appear that consumer staples is a clearcut winner.



Tufte soundbites

Tom B. alerted me to an interview with Ed Tufte, by Ad Age (link). It's a good read. The journalist attended one of Tufte's courses but then the interview was conducted via email. So it reads like a condensed version of Tufte's writing, stuffed with his many colorful coinages.

I like this comment related to Big Data:

First: "overwhelming data" is a bit of a hoax. Many of the time measurements have enormous serial correlation (just because you can measure to the millisecond doesn't mean you've learned anything about a process that moves to a monthly rhythm) and extreme high collinearities in the things measured (as in the endless web metrics, many of which are measuring the same thing over and over). Finally, most website data bizarrely and deliberately overstates the extent and intensity of website activity.

Pets may need shelter from this terrible chart

Josh tweeted quite a shocking attack ad to me last week. He told me it came from the DC Metro. The ad is taken out by a group called HumaneWatch.Org, which apparently is a watchdog checking up on charity organizations. The ad attacks a specific group called the Humane Society of the United States. Here is the map that is the centerpiece of the copy:


Trifecta_checkupI like to use the Trifecta checkup to evaluate graphics. It's a nice way to organize your visualization critique. You progress through three corners: figuring out what is the practical question being addressed by the graphic, then evaluating what data is being deployed, and finally whether the graphical elements (the chart itself) is well executed in relation to the question and the data.


Based on the map, it appears that HumaneWatch is interested in the spending on pet shelters. Every number shown is tiny: on a quick scan, the range may be from 0% to 0.35%. The all-caps title "A Whole Lotta Nothing" confirms that this is the intended message.

Knowing nothing about either of these organizations leaves me confused. Should the "Humane Society" be spending the bulk of its budget on pet shelters? If it doesn't, is it because the staff is pilfering money, or because it has wasteful spending, or because pets are not its major cause, or because pet shelters are not the key way this organization helps pets?

I did look up Humane Society to learn that it is an animal rights group. The four bullet points at the bottom of the ad provide a clue as to what the designer wanted to convey: namely, that this charity is a scam, with too much overhead spending, and spending on pensions.


So I think the question being asked is sufficiently clarified, and it's a pretty important one. How is this organization spending its donations? Is it irresponsible compared to other similar organizations?


The data should be in sync with the question being addressed; that's why there is a link between the two corners of the Trifecta. Given the trouble I endured understanding the question being addressed, it would come as no surprise that this chart scores poorly on the DATA corner.

I don't understand why budget spent on pet shelters is the key bone of contention. Based on the perceived objectives, it seems that they should display directly what proportion of the budget went to overhead, and what proportion went to pensions, with suitable comparisons.

The analysis by state is a disease of having too much data. Let's imagine that the proportions averaged across all states come to 0.1%. If we replaced those 50 numbers with one number printed across all states: "The Humane Society spends less than 0.1% of its budget on pet shelters.", the message would have been identical, while being less confusing.

And it's not just confusion. Cutting the data by state introduces complications. The analyst would need to make sure that any differences between states are not due to factors such as the number of pets, the proportion of households owning pets, the average spending per pet, the supply and demand for pet shelters, the existence of alternatives to pet shelters, etc. None of these issues need to worry the designer who does not slice the data down.

The same reason goes for why the absolute amount of spending (encoded in the colors of individual states) is not worth the ink it's printed on. The range between 0% and 0.35% has been chopped into seven pieces, which creates artificial gaps between the states. This design muddles the graphic's key message, "A Whole Lotta Nothing".


As we land on the final corner of the Trifecta, we ignore our previous complaint and accept that the proportion of budget is an interesting data series to visualize, and turn attention to the graphical elements. This chart scores poorly on chart execution as well!

Notice that the designer simultaneously plots two data series on the same map, the dollar value of pet shelter spending, and it as a proportion of budget. The former is encoded in the color of the state areas while the latter is printed directly as data labels. This is a map equivalent of "dual-axes" line charts, and equally unreadable.

Dcmetro_map_colorsBased on the color legend, our brain tells us the yellow states are better than the blue states but the huge numbers printed on the map conveys the opposite message. The progression of colors makes little sense. The red and yellow stand out but those states are in the middle of the range.

It's a little blurry but I think there is a number of New England states in the high spending category (black and dark gray colors), and the map just happens to obscure this key feature.




DATA: Very Poor


An inspired picture of Blackberry's dying inspiration

The New York Times has a splendid example of an infographics this weekend, showing the rise and fall of the Blackberry.


Notice the inspired touch of the black circles to trace the outline of Blackberry's market share. They are a guide to experiencing the chart.

I wish they had put the Palm section above Blackberry. In an area chart, the only clean section is the bottom section in which the market share is not cumulated. Given the focus on Blackberry, it's a pity readers have to perform subtractions to tease out the shares.

I also wonder if the black circles should contain Blackberry's market share rather than the year labels.

But I enjoyed this chart. Thanks for producing it.


Highway Safety Agency goes rogue

A reader sends me to Adam Obeng, who did the dirty work deconstructing a set of charts by the U.S. National Highway Traffic Safety Administration on his blog. Here's an example of these charts:


Aside from the sneaker chart, they concocted a pop stick, a pencil, a tower of Hanoi, etc. These objects are ones I think should be evaluated as art. Adam gamely tells us that the proportions are totally off, and they are both internally and externally inconsitent.


I'll add two small points to Adam's post.

First, these charts pass my self-sufficiency test, that is to say, they did not print the entire data set (just one number here) on the page. Alas, given the distortion identified by Adam, not printing the data means everyone is free to create their own data. Herein lies the problem: there is an argument for allowing a small degree of distortion in exchange for "beauty" but these charts without any data have gone too far.

Second, see Adam's last point (the footnote). The original data is something quite convoluted: “3 out of 4 kids are not as secure in the car as they should be because their car seats are not being used correctly.” (How would they know this, I wonder.) This is a statistic about kids while the picture shows a statistic about their parents (or drivers).