The 90-Minute Rule

“Human beings aren’t designed to expend energy continuously. Rather, we’re meant to pulse between spending and recovering energy.”
– Tony Schwartz (source)

In the late 1950’s, researchers William Dement and Nathaniel Kleitman documented that humans sleep in 90-minute cycles – from light to deep sleep and back to light sleep again. Professor Kleitman later discovered that a similar pattern happens when we are awake – from a state of alertness to physiological fatigue. After 90 minutes of work, we start to feel tired and less productive. Essentially, this is our bodies telling us that we need to slow down and take a break.

Unfortunately, we often ignore these biological signals and try to mask the impact with stimulants like caffeine and sugar. When we’re on a tight deadline or under lots of pressure, we might also summon up the stress hormones adrenaline and cortisol. These stimulants work in the short term but they usually lead to an inevitable feeling of crashing.

Research by Professor K. Anders Ericsson on elite performers reinforced the 90-minute cycle. Professor Ericsson found the best violinists daily practice usually consisted of three sessions of 90 minutes each with breaks for recuperating between each session. These elite musicians rarely worked for more than 4.5 hours per day and they slept almost an hour more than average musicians. A similar pattern emerged in studies of athletes, actors, and chess players.

More from Professor Ericsson:

To maximize gains from long-term practice, individuals must avoid exhaustion and must limit practice to an amount from which they can completely recover on a daily or weekly basis.

I call this effect the 90-minute rule and recommend applying it to your daily routine, regardless of your profession. You should schedule your day in 90-minute periods of intense, uninterrupted work with 20-30 minutes of renewal breaks between them. You should also make sure business meeting don’t last longer than 90 minutes. When you plan an offsite or a conference agenda, no session or presentation should be longer than 90 minutes or else you risk losing the audience.

Adhering to the 90-minute rule takes discipline, especially since we live in an always-on society. But the science tells us we can be better performers if we give ourselves enough time to recuperate. Take a walk, eat a healthy snack, or even chit-chat with co-workers.

And the next time your boss asks if you’re goofing off at work, refer to the 90-minute rule and say you’re taking a science-mandated break.

When Promoting People, Beware the False Record Effect

After writing about several examples of bias from insensitivity to sample size, a former colleague asked whether I thought performance in the workplace was subject to the same bias. She observed that people were sometimes rewarded or even promoted for high performance, even if that performance was sporadic rather than sustained. She asked:

Shouldn’t the promotion of an employee be tied to a larger sample size (i.e. more time)?

The answer is heavily nuanced. In my own experience, performance at work is not solely based on your own skills. A variety of external factors come into play, including the difficulty of your tasks, the quality of your team, and even some luck. The more time you have to observe an employee, the more you can tell how much impact these external factors had.

James G. March, Professor Emeritus at Stanford University, describes this as the False Record Effect:

A group of managers of identical (moderate) ability will show considerable variation in their performance records in the short run. Some will be found at one end of the distribution and will be viewed as outstanding; others will be at the other end and will be viewed as ineffective. The longer a manager stays in a job, the less the probable difference between the observed record of performance and actual ability.

This doesn’t necessarily mean that seniority is a better way to determine promotions. Organizations that favor “time in seat” over short-term success lose the benefit of fresh perspectives and can become stale.

We are more susceptible to the False Record Effect early on in an employee’s career when there is less performance data available to make decisions. As Professor March cautions:

Within a group of managers of varying abilities, the faster the rate of promotion, the less likely it is to be justified.

In other words, the fast start might be an anomaly and the employee could have a regression toward the mean.

So, what should an organization do? The key is balance. In business as in sports, recognize that we likely will be biased by the hot hand fallacy. When deciding on a promotion, don’t just consider an employee’s performance but take into account the difficulty of his/her assignments.

When in doubt, my recommendation is to promote those who have a history of making others better, not just themselves.

4 out of 5 People Can Be Wrong

The law of small numbers (or hasty generalization) is the tendency to jump to a conclusion without enough evidence. In statistics, it’s called bias from insensitivity to sample size – generalizing from a limited number of events (a sample) selected from a much larger number of events (the population). For example, if a mutual fund manager has three above-average years in a row, you might conclude that the fund manager is better than average and will have a fourth above-average year. While it may be true, you cannot come to this conclusion from such a small amount of data.

This bias shows up frequently in the media. Imagine the headline summarizing a telephone survey of 500 rural inhabitants, in which 70% responded they didn’t have enough money for retirement. It would likely be something like this: People living in the country can’t comfortably retire. Most readers would ignore the details of the survey, including whether 500 people is sufficient to warrant the conclusion.

This is the challenge with small sample sizes. If the survey had only included 50 people, you’d be suspicious. And if it included 50,000 people, you probably wouldn’t be worried. But what about 500? Our intuition is good at the extremes but not in the middle. Without more information, it’s hard to know if the 500-person sample size is sufficient.

In the book Thinking, Fast and Slow, Daniel Kahneman elaborates:

The exaggerated faith in small samples is only one example of a more general illusion – we pay more attention to the content of messages than to information about their reliability, and as a result end up with a view of the world around us that is simpler and more coherent than the data justify. Jumping to conclusions is a safer sport in the world of our imagination than it is in reality.

Here’s a test of your sensitivity to sample size, courtesy of Max Bazerman in Judgment in Managerial Decision Making: A town has two hospitals; 45 babies are born each day in the larger one and 15 are born in the smaller one. Overall about 51% of babies are boys but of course the exact percentage varies day to day. During one year, both hospitals tracked the number of days in which more than 60% of the babies born were boys. Which hospital had more of these days?

A. The larger hospital
B. The smaller hospital
C. About the same (within 5% of each other)

Did you guess C? Most people incorrectly choose C when the right answer is B. Having 60% boys in one day is a rare event and statistics tell us that we’re more likely to observe a rare event in a small sample than in a large one.

You can also see this effect in sports. In a sport with a long season like basketball, hockey or baseball, the standings 20 games into the season may not be representative of the final standings. But at the end of the season, the best team usually has the best record. This effect is why sports fans are often optimistic; on any given day, anything can happen – especially in the playoffs. As Michael Lewis writes in Moneyball: “In a five-game series, the worst team in baseball will beat the best about 15% of the time.”

Which brings us to advertising. The classic commercial which claims “4 out of 5 dentists recommend…” has been mimicked many times over the years. The law of small numbers teaches us this claim is meaningless unless we know the sample size.

My guess is that there were only 5 dentists. Which means they could be wrong.

The Red Queen Effect Explains Why You Aren’t Getting Ahead

For much of my career, I’ve argued that people design key performance indicators (KPIs) incorrectly. One of my own favorite blogs made the case that, unless you compare yourself against some external benchmark, you might be making progress towards achieving your KPIs but actually losing ground. Simplistically, if you’re growing by 20% and the market is growing by 30%, you’re losing market share.

I was explaining my performance management approach to a class on digital disruption when a student pointed out that I was referring to the Red Queen Effect. For those who aren’t familiar with the concept, it comes from Lewis Carroll’s classic book “Through the Looking Glass.” At the Red Queen’s urging, the heroine Alice runs faster and faster but never seems to get anywhere. Here’s a short passage:

The most curious part of the thing was, that the trees and the other things round them never changed their places at all: however fast they went, they never seemed to pass anything.

[…] Alice looked round her in great surprise. ‘Why, I do believe we’ve been under this tree the whole time! Everything’s just as it was!’

‘Of course, it is,’ said the Queen, ‘what would you have it?’

‘Well, in our country,’ said Alice, still panting a little, ‘you’d generally get to somewhere else — if you ran very fast for a long time, as we’ve been doing.’

‘A slow sort of country!’ said the Queen. ‘Now, here, you see, it takes all the running you can do to keep in the same place. If you want to get somewhere else, you must run at least twice as fast as that!’

It’s not enough to run faster or perform better than you have in the past – you must be faster/better than those around you. But it’s unlikely you can continually exceed everyone else. Strong over-performance is usually followed by average or even under-performance. This is reversion to the mean.

You can see the Red Queen effect with Darwin’s survival of the fittest. In evolutionary theory, those that leverage their ‘strengths’ by adapting to their surroundings are more likely to prosper. This puts them ahead of their competition and therefore more likely to ‘survive.’ In many cases, however, these advantages are often temporary.

Physicist John Gribbons entertaining book ‘Get a Grip on Physics’ provides a vivid example of the Red Queen effect with frogs and flies:

Suppose there are frogs that eat a certain kind of fly, which they catch by flicking out their tongues. If the frogs evolve a particularly sticky tongue, they will be adept at catching flies. The frogs will do well, and the flies badly, in the evolutionary stakes.

But if the flies evolve a particularly slippery body surface, they will be able to escape from the sticky tongue more easily – and the original balance will be restored. […] Overall, nothing has changed. There are still the same number of frogs, each other eating the same number of flies.

I see many similar examples in business. Companies continually try to out-innovate each other or run continuous promotions to compete on price. One company might gain a temporary advantage but eventually others catch up.

Today’s Silicon Valley seems to be suffering from the Red Queen effect. Every venture capitalist seems to be funding variants of the same business plan. Every company seems to provide similar benefits like free lunches. Everyone is working crazy hours.

Is anyone really getting ahead?

We live in a multiplicative system but don’t know it

Remember the saying that a chain is only as strong as its weakest link?

It’s a concept we inherently understand: if we pull on the chain, the weakest link will fail first which breaks the entire chain. Sometimes people invoke the metaphor to explain why we should concentrate on helping the lowest performers in a group as the best way to improve overall performance, rather than relying on the superstars.

This metaphor is based on a multiplicative model, rather than an additive one. I will use two questions and a little math to explain what I mean:

Q1: What’s 6347 times 34 times 0 times 812?
Q2: What’s 6347 plus 34 plus 0 plus 812?

You probably recognized quickly that the first answer is 0 but it may have taken you a little while to calculate the second answer (7193) – especially if you did the math in your head. The 0 is the weakest link in the multiplicative chain because it makes all of the other numbers irrelevant.

Additive systems are different. Imagine you are throwing a potluck dinner. You cook the main dish but ask your guests to bring the salads, sides, and desserts. You don’t need to dictate which person brings which dish. If every brings salads and sides and no one remembers to bring a dessert, you still have a successful potluck dinner. No individual dish is the weakest link in a potluck; it’s an additive system. (You could claim the main course is the weakest link but I believe you still have a successful potluck with sides, sales, and desserts but not a main course.)

Most businesses think they operate in an additive system but they really operate in a multiplicative one. We’ve all interacted with a company that continually promotes their new products but has lousy customer service. No matter how good the product is, customers don’t come back because of the service – the bad service acts like the zero in the multiplicative chain making the good product (and everything else good about the company) irrelevant. Therefore, rather than investing in product, the company should focus on improving service.

For decades, classically-trained marketers have known multiplicative thinking is required to be successful. Introduced by Regis McKenna and popularized by Geoffrey Moore in Crossing The Chasm, the so-called ‘whole product’ reminded marketers that the core offering is not sufficient to delight customers. There must be complementary offerings, training, support, channel and many other things to create the whole product.

A multiplicative system also comes into play when you are trying to get a job, especially early on in your career. You might have strong skills, good experience, and supportive references but you might not be a cultural fit for the organization. Or maybe you’re already at the company but have a reputation for self-promotion. Regardless, one weakness among many strengths is enough to hold you back.

It’s a lesson we all need to remember in business: find the weakest link in the chain and focus on strengthening it.