Tag: average lies

Jurassic Park (book review)

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Jurassic Park (1993) by Michael Crichton is a book about expectations. But first, we have to address the movie.

The movie was great. It was an amazing adaptation (and is connected to the Pixar story btw). But – it defines the characters. Hammond, Malcolm, Ellie, and Grant are the movie version in my version. Oh well.

Ok, back to expectations.

The Jurassic Park story turns when Malcolm tours the facilities and sees this:

See, Hammond says to Malcolm, everything here is normal!

But, Malcolm counters “that is a graph for a normal biological population. Which is precisely what Jurassic Park is not. Jurassic Park is not the real world.”

Normal distributions (and averages) are a specific tool. But they are the wrong tool for distinguishing between Snickers and Milky Way, student loan debt, or Aaron Rodgers touchdown passes. Or, tracking dinosaurs.

Jurassic Park is not the real world. It is a zoo. Cages. Fences. Pens. Controlled feeding. Controlled breeding (oops). Controlled everything.

Malcolm again, “Because the history of evolution is that life escapes all barriers. Life breaks free. Life expands to new territories. Painfully, perhaps even dangerously. But life finds a way.”

Life finds a way.

“Now you see the flaw in your procedures,” Malcolm said. “You only tracked the expected number of dinosaurs. You were worried about losing animals, and your procedures were designed to advise you instantly if you had less than the expected number. But that wasn’t the problem. The problem was, you had more than the expected number.”

Hammond expected to run a zoo.

Hammond expected a ‘normal number’

Hammond expected his problem to be ‘fewer’ not ‘more’.

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Expectations are heavy, they are hard to throw off. I could only picture Jeff Goldblum as Dr. Ian Malcolm. Hammond could only picture Jurassic Park one way too.

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This isn’t really a book about dinosaurs, they’re just a stand in. For what?

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Also interesting that Waltrop’s Complexity came out around the same time. Something was bubbling in the early 90s. Something is bubbling now too.

95/5 Instead of 50/50

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It’s 2004. Will Guidara is working at the Museum of Modern Art. Not in the esteemed gallery or adored restaurant. Will is in charge of the cafe: coffee, sandwiches, and snacks.

And he wants to create a gelato cart for the Sculpture Garden.

But first, he needs gelato worthy of the museum and his group, Union Square Hospitality. He finds Jon Snyder who sells it at a discount. He also convinces Synder to pay for the cart. It’s a nice cart.

Things are looking promising.

And then Guidara goes crazy.

He wants Italian spoons. “How amazing could a plastic spoon possibly be?” Will writes, “You’re going to have to trust me on this: they were paddle-shaped, extraordinarily well designed, and completely unique.”

But they’re expensive. His boss sees the cost and says “we’ll talk about this later.” But Guidara loves them. He gets them. He creates The 95/5 Rule.

“Manage 95% of your business down to the penny; spend the last 5 percent ‘foolishly’.”

This idea manifested later when Guidara was at Eleven Madison Park. While traditional wine flights had average wines, Will and his winos wander wider. Most of the samples were good, diverse, and less expensive. But one, the last one, was excellent. “The Rule of 95/5 gave us the ability to surprise and delight everyone that ordered those pairings, making it an experience they would never forget.”

It’s a good rule because averages are not good measures. Save where you can but splurge on one thing. That’s helpful. That gets past average thinking.

The End of Average (book review)

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If markets have a limited supply but high demand then prices will be high. Disney vacations are one example. Human capital is another. Computer science majors earn the highest salary out of college and humanities majors earn the least. Employers distinguish students (supply) by their degrees.

But how do you distinguish among the computer science majors? The answer is included in Todd Rose’s 2017 book, The End of Average.

Rose’s big idea is economic – society overpays for talent!

Throughout the 1800s and 1900s, modernization has been an experience of measurement. At first, the outcomes were crude because the measures were crude. Take the twenty years of Moneyball progress and stretch that through two centuries. In the same way that baseball teams overpaid for home runs, society overpays for talent.

Rose offers three explanations for our mistake.

1/ Jaggedness. What makes a good first baseman? That depends. What makes a good leader? That depends too. Unfortunately, nuance is neglected in our day-to-day functions. We tend to use loss-aversion-based heuristics. When you evolve from mammals focused on danger, food, and sex there’s only so much digging our default allows.

Winston Churchill is an example of a jagged leader. He excelled in oration and “stature” but less in collaboration. During the war, certain skills were more important than others. This brings us to…

2/ Context. Brent Beshore’s people are messy comment summarizes Rose’s idea. Instead, think of people as complicated creatures who act using If/Then statements. Someone may be honest or careful or diligent based on the situation.

We miss this, Rose writes, because our samples of other people aren’t wide enough. Jessica from the office may act snooty or kind at work – the only place we see her. But does that encompass her at church, at home, and with her family?

3/ Paths. There are not a million ways to do something, Rose writes, but there’s also not one. Think of a situation like being lost in the forest. The goal is to get out. One option is to find the path and follow it. But one could forge their own as well. Too often the focus is on the path and not forging a way out.

If a group undervalues these explanations then it restricts the possible outcomes. Imagine a rule that in order to start a business someone had to give up listening to podcasts. There are a lot of great business podcasts and the budding entrepreneur would be worse off – and so would we, missing out on the upside of their creation.

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The End of Average is a Bob Moesta book suggestion and reading it from his point of view offers additional information.

Moesta is a product designer, researcher, and marketer. Put on that POV and we can see how products fit within Rose’s explanations as well. Our hunger is jagged, hence the difference between Snickers and Milky Way. Our purchases are context-based, Moesta comments that hot dogs and steaks are both the right meal for the right context. Lastly, consumers end up at a product in a variety of ways, there’s not a single sequence of “I need a new car”, but there’s not an infinite either.

My first impression of The End of Average was that I kinda already understood these topics and didn’t need to spend time on the macro-educational angle. Both impressions were true but there were deeper ideas too and giving names to jaggedness, context, and paths is and will be helpful.

Snickers and Milky Way

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Snickers and Milky Way

Reframing our perspective is a powerful thinking tool. ‘Sleeping on it’ is reframing. Reading books is reframing. Comparing novel things is reframing. 

For a business owner, thinking of time of day, place in life, and what happened prior is reframing.

Bob Moesta notes “context creates value”. Time and place create more or less value. Birthday gifts have one value on birthdays and another value when it’s not. 

But we miss this because of average lies. Average computes easily, is sometimes helpful, but is a crude tool. Sometimes we NEED this one thing RIGHT NOW! 

Contrast Snickers and Milky Way. Graphically: 

Commercially (2011):

Snickers is a chewy pick-me-up energy bar. Milky Way is a treat-yo-self deep breath of sweetness. The context creates value. 

According to Bob Moesta, the context for eating Snickers is that I’m hungry and I want something filling, tasty, cheap, and fast. Applying average thinking, there’s not a constant demand. Find when customers consume a product reveals that product’s JTBD.

“Context creates value” fits well with Alchemy too. Channeling Rory Sutherland, it wasn’t that Snickers needed to be tastier, rather reframed. Alchemy is about solving problems with psychology rather than physics. Instead of making travel faster, make it more enjoyable with wifi, charge ports, booking flexibility, a table for tea, someplace for the kids to burn off energy, and so on. Faster is only better when the process sucks. 

Consumers and customers have untapped wants. They’re hiding behind time, place, averages. They’re served by JTBD & Alchemy. 

$1 Toronto real estate

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Average is like my reciprocating saw: never as useful as I expect. Part of the reason average sticks around is economics, It’s cheap to produce.. Average is a crude tool, like with student loan debt, and often hides the heterogeneity of a situation.

We’re entering an era of precision. One covid lesson has been the effect size of heterogeneity. At the macro level, the impact of covid depends on time and place. At the micro level, the impact depends on age, immunity, and social network. Covid was (is?) difficult to judge because there are a lot of factors that need fitted together.

If we need precision we should probably think about distributions at least as often as we think about averages. An example is the periodic one dollar real estate listing. Yes, this generates attention, spins up the market mechanism, and might be the marketing magic an owner needs. But it also changes the distribution of offers without changing the average offer.

“When you give people a listing price they ask if it’s worth more or less and by how much, so they anchor at the listing. If you don’t have an anchor people build a valuation from first principles. The average (offer) doesn’t change but the distribution does. For a one dollar listing you get some really high rates and some really low ones. In the listing price you get distributions around what the asking price was. This is a world where the seller doesn’t care about the average, they only care about the top end of the distribution.” – Dilip Soman, The Decision Corner, October 2021

Maybe this is being too hard. Average, like the saw, has its uses. The aim here is to combine numeracy with psychology to get by in the world. That means presenting the ‘best’ wait times or predicting rain more often. Being numerate is understanding that the average age is 78, but if you make it to 65 you’ll probably live well past eighty.

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“The average looks like 10-12 years lost due to Covid – but that’s an average of a distribution with a very odd shape, a highly skewed distribution, some people have lost forty years of life. The peak of the distribution is people who lost less than a year of life.” – David Spiegelhalter, Risky Talk October 2021

Average Lies 2

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Edit: see the comments about the original purpose of the VCR and why the clock was important. That said the overall idea still stands and applies to IOT, cameras, chips. 

When I was a kid there was something I didn’t get. Why did VCRs have a clock? The thing never seemed to work correctly, was slightly different from every other clock in the house, and wasn’t central to the functionality of the VCR unit. I was going to watch True Lies (again) and did not need to know what time it was.

giphy

As an adult I think I get it. The VCR had a clock because clocks were inexpensive to install. A few cents may not make sense from a JTBD approach, it does make sense from a sales approach. When people compare A to B at the same price but A has something B does not (a clock), consumers will choose A even if it’s a feature they don’t really need.

Like a VCR clock.

The same thing happened with pictures. Instagram changed not only how people took photos but how many. Pictures are cheap to take and share. As things become digitized they are cheaper.

Like numbers.

More counts, more code, more algorithms, more nodes. The network grows and the network shows everywhere that Mikey-boy goes. 

We are counting more and computing more which means we will be sharing more numbers. This Average Lies series (part 1) is a reminder to dig deeper into numbers and come up with a framework for when average is, and is not a good measurement.

  • Good: Biological (height). Mediocristan. Large samples. Homogeneous.
  • Bad: Social (media). Extremistan. Small samples. Heterogeneous.

Here are three more examples:

On The Long View, Moshe Milevsky said, “The number of times you’ve circled the sun, your chronological age, doesn’t really reflect the years you have remaining. You can be fifty-five years old chronologically, and I can be fifty-five years old chronologically but that doesn’t really tell us how long we have to spend in the lifecycle.”

Tuscan is cooler but south of Phoenix.

The most successful country in the NBA? Poland of course.

As numbers are cheaper to produce more numbers will be produced. Like VCR clocks and Instagram pictures, some will be good but some will not.

Want more? Check out this pay-what-you-want placebo prescription pdf.

Average Lies

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“Often an average is such an oversimplification that it is worse than useless.” – Darrell Huff, How to Lie with Statistics.

We don’t really think about averages. The average hospital costs for hepatitis A was $16,000 in 2017. The average student loan debt for North Carolina residents is $36,000. The average American says they’ll spend $142 on Valentine’s gifts. Men, on average of course, say they’ll spend more than women.

For some things in life, average is fine. When my daughters were born, the hospital gave us a growth chart for their height and weight. It showed deciles and right in the middle was average. Growth charts are simple. Height. Weight. Plot. On chart meant on track, physically at least.

Now my daughters are twelve and ten and wow how things changed. New parents can track their child’s sleep, diet, movement—bowel or otherwise. And it’s not just parents. Everyone can track their taken steps, hours slept, and Spotify streams.

With technology, counting is easier.

With counts, averaging is easier.

Numbers are tools. Rather than bartering bananas for bread we have dollars and cents. With numbers, stores count their bananas bundles. With numbers, people have balanced budgets.

Numbers are tools. Like other tools, they take practice with feedback to build proficiency. I’m much more careful with the occasional use of power tools than the regular use of a chef’s knife. Numbers are like that. Well practiced and well used, numbers are a unique and powerful tool.

An example of numbers telling another story was the sabermetrics revolution in baseball. Smart teams realized that walks are better than hits, and that walks cost less to buy. Worth more, cost less. It’s like the successful Miller Lite advertising campaign: ‘tastes great, less filling’.

Decades later, sabermetrics happened in basketball with the insight that making one-third of three-point shots was the same as making one-half of two-point shots. Life, like sports, uses numbers more.

Numbers, though hidden in code, will become more prevalent in life and more important. 

Average, as numbers go, is often abused. This is due to many reasons, but just like technology has reduced the cost of tracking a baby’s bowel movements, average is used because the cost is low. It’s sixth-grade math. And it can hide important nuances.

For example, the average student loan borrower owed $28,000 in 2016. If we dig a bit deeper we find:

  • The median debt was $17,000.
  • The median for two-year degrees was $10,000.
  • The median for a four-year degree was $25,000.
  • One-in-four borrowers owed less than $7,000.
  • Only 7% of borrowers owed more than $100,000.

Those details are often omitted from the story. One poll showed that people viewed median debt of $17,000 as the “least bad figure about student loans”. Life is nuanced but numbers are not. Framed influences the way numbers are understood.

Thanks for reading.