Sketchplanations
Big Ideas Little Pictures

Sketchplanations in a book! I think you'll love Big Ideas Little Pictures

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Explaining the world one sketch at a time

Survivorship Bias illustration: two analysts observe bullet damage, depicted by red dots, on a fighter plane returning from battle. They propose that heavier armour is added to all aircraft in the most hit areas of the plane. A third onlooker wonders whether this surviving plane tells a different story in that it returned because the most vulnerable areas weren't hit. Maybe heavier armour should be added to those areas?

Survivorship bias

So the story goes, as told by Stephen Sigler, Nature May 1989: "The US military studied fighter planes returning from missions to try to improve their survival rate and were considering adding heavy armour to those parts of the plane that tended to show the greatest concentration of hits from enemy fire, until statisticians pointed out the fallacy of that argument. The more vulnerable parts of the plane were those with the fewest hits; planes hit there tended not to return at all. The single most vulnerable part, the pilot’s head, was without serious scar in the sample of planes that returned." It’s easy to draw correlations from what we see in front of us. But what we see usually represents just a small part of what has happened. Focusing on the evidence we can easily see at the expense of that we can’t leads to survivorship bias. To say it another way, when Bill Gates drops out of college and starts Microsoft it might seem like dropping out is a path to success for others too, but that ignores all the dropouts who didn’t create Microsofts and consequently you didn’t hear about. Silent evidence — the evidence that we don’t or can’t easily choose to consider — is a term from Nicholas Nassim Talleb. When trying to discover the veracity of the planes story I enjoyed Bill Casselman’s American Mathematical Society article on The Legend of Abraham Wald. The postscript points to some of the source behind the story including a mention of Stephen Sigler’s letter in Nature quoted above. I covered survivorship bias before, but I like this story so much I thought it was worth doing again.
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Finishing Lines, Stevie Smith quote illustration: a cyclist sits on their bike about to set off on a long journey - the bike is heavily loaded with panniers at the front and the back. The thought bubble above their head shows them visualising the finish line of their journey as their motivation to get going. As Stevie Smith suggests: "The most important role of a finishing line is to get you over the start line."

Finishing lines

“Just remember that finishing lines are good, but their most important role is to get you over the start line in the first place.” This little piece of wisdom is from Stevie Smith (not the poet). Stevie embarked on a human-powered journey around the world—Expedition 360—and reached Hawaii under pedal power*. I learned about him from a little chapter in One Man and His Bike by Mike Carter. As soon as I finished the chapter where he speaks with Stevie I immediately reread it. Mike also shared this deeper and more poetic version of the same insight: “Happiness is the acceptance of the journey as it is now, not the promise of the other shore” *Stevie’s companion Jason Lewis completed the circumnavigation of the world 13 years after they began. This quote, modified to a lovely two-page spread, features with a number of others in my book Big Ideas Little Pictures. You can also listen to us discuss this sketch with the brilliant Brendan Leonard in the Sketchplanations podcast episode on Finishing lines Also see: Forcing function The doorstep mile
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Hope: CR Synder's model for hope illustrated by a climber looking at The Ogre mountain considering the goals, pathways, and willpower that gives them hope they can climb it

Hope

I hadn’t given much thought to hope, beyond it being a nice feeling to have, until I heard of CR Snyder’s cognitive model for hope which shed a new light on it for me. He proposed a model of hope where (paraphrasing) an individual may be hopeful if they have: Goals they desire. If you don’t or can’t picture any future state you’d like then you won’t have a lot of hope. Pathways. You need to see some ways that you may make step-by-step progress towards a goal. Willpower or agency. You need to be motivated and believe that you have the ability to succeed at your goal. With all of these you can imagine feeling hopeful. Without any one of them and you probably won’t so much. The mountain is the Ogre, a wholly impossible looking peak scaled by Doug Scott and others for which I would have had zero hope to climb (no pathways, no willpower) and yet they remarkably did. HT once again to Brené Brown in Dare to Lead.
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Sexy value illustration: the Venn diagram overlap of better and cheaper

Sexy value

Sexy Value is what you get when someone can launch something that is not only cheaper but also better. It’s a near irresistible proposition. Most new things that are better don’t tend to also be cheaper—increased quality usually has a corresponding increase in cost. So Sexy Value is often made possible through a new technology or approach. It’s like when the first taxi apps started appearing. To me, the first experience was just so much better than what I was used to—no trying to catch a taxi in the rain, seeing when the cab would arrive and simple, non-awkward, no swinging by the ATM payments—and yet at the same time, it was also cheaper. Sexy Value is not the only way to compete. If you make something better, people will pay for the value it provides them. But you’re on to a winner if you can also do it cheaper. Sexy Value is a name, I believe, from Ogilvy and Mather. You might also like another model of theirs: The Big Ideal
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Solstice: illustration showing the paths of the sun in winter, summer and on the equinoxes

Solstice

From the combination of sol (sun) and sistere (cause to stand or stop) the solstice marks the highest or lowest points of the sun’s path in the sky each year. In summer, for instance, the sun’s arc in the sky stops its daily ascent at the longest day of the year. There it reverses, beginning its descent to the autumnal equinox — with equal day and night — and eventually to its lowest point at the year’s shortest day, the winter solstice. At that point, its descent finished, the sun once again begins its slow climb towards the summer solstice. Also see: Equinox Seasons
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Analytics Maturity illustration: A young character is very happy, sat at their makeshift lemonade stand next to the sidewalk. When they close their stand, our young entrepreneur can assess their performance today using hindsight and insight to inform preparation through foresight for next weekend.

Analytics maturity

There are lots of questions you can ask of data. I like this model as it gives a framework for the different types of questions you might ask. It was introduced to me as the data science pyramid with descriptive analytics — measuring what’s happening — being the foundation on which others are built. You don’t have to go in order, but it’s a nice approach to consider how the questions you’re asking of your data might evolve. To take our school Summer fête as an example, for a long time takings were all in cash and put into one bucket at the end for counting. You end up with one number for how much you raised. But you might move to collecting more descriptive data on the takings for each stall — whether the bar earns more than the tombola, for example. You then might be able to ask diagnostic questions like figuring out why one fête earned more than another with a different mix of stalls. If you know how many people came you might be able to make some predictions on the takings of a future fête based on number of people and stall types. And if you get fancy you might start to use your data to figure out how you could raise more money for the school in future. There are many ways to slice and dice data and the questions you ask, but I like the clarifying simplicity of this one. As far as I know, the framework is the Analytics Ascendancy model, or Analytics Value Escalator, or other such business sounding name from Gartner.
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