Chapter 16

The List

1. The family with 1½ children. Often the average scenario, like the average family with 1½ children, is nonexistent. For example, a bank may have two main groups of young customers: students with an average income of $10,000 and young professionals with an average income of $70,000. Would it make sense for the bank to design products or services for customers with the average income of $40,000?

2. Why everything is behind schedule. Recall the problem of getting you and your spouse to the VIP reception on time or the software project with ten parallel tasks that each averaged six months. Setting each task at its average results in project completion in six months, but the chance that all ten come in at their average or sooner is the same a flipping ten heads in a row, so the chance of finishing by six months is less than one in a thousand.

3. The egg basket. Consider putting ten eggs in the same basket, versus one each in separate baskets. If there is a 10 percent chance of dropping any particular basket, then either strategy results in an average of nine unbroken eggs. However, the first strategy has a 10 percent chance of losing all the eggs, whereas the second has only one chance in 10,000,000,000 of losing all the eggs (in case you still question the wisdom of diversification).

4. The risk of ranking. When choosing a portfolio of capital investment projects, it is common to rank them from best to worst, then start at the top of the list and go down until the budget has been exhausted. This flies in the face of modern portfolio theory, which is based on the interdependence of investments. According to the ranking rule, fire insurance is a ridiculous investment because on average it loses money. But insurance doesn’t look so bad if you have a house in your portfolio to go along with it.

5. Ignoring restrictions. Recall the microchip example involving an investment in infrastructure sufficient to provide capacity equal to the average of uncertain future demand. If actual demand is less than average, profit will drop. But if demand is greater than average, the sales are restricted by capacity. Thus there is a downside without an associated upside, and the average profit is less than the profit associated with the average demand.

6. Ignoring optionality. This is exemplified by the natural gas property, with known marginal production costs but an uncertain future gas price. It is common to value such a property based on the average gas price. If gas price is above average, the property is worth a good deal more. But if you have the option to halt production if the price drops below the marginal cost, then there is an upside without an associated downside, and the average value is greater than the value associated with the average gas price.

7. The double whammy. Consider the earlier example of the perishable antibiotic with uncertain demand, in which the quantity stocked was the average demand. If demand exactly equals its average, then no costs are associated with managing the inventory. However, if demand is less than average, then there will be spoilage costs; if demand is greater than average, there will be air freight costs. So the cost associated with average demand is zero, but average cost is positive.

8. The Flaw of Extremes. In bottom-up budgeting, reporting the 90th percentile of cash needs leads to ever thicker layers of unnecessary cash as the figures are rolled up to higher levels. Even more harmful things result from focusing on above- or below-average results, such as test scores or health-related statistics. A full explanation appears in Chapter 17.

9. Simpson’s Paradox. Can you imagine a nutritional supplement that on average causes people to lose weight? The only exceptions are people who are either male or female, in which case, on average, they gain weight. In Chapter 18 I will explain how this bizarre statistical situation can occur.

10. The Scholtes Revenue Fallacy. Suppose you sell different quantities of various types of products, each with its own profit per unit. You might make a nice profit on your average product and yet lose money overall, as discussed in Chapter 19.

11. Taking credit for chance occurrences. We all like to take credit for our hard work, but some successes may be due to dumb luck. Chapter 20 can help you tell the difference.

12. The Winner’s Curse and Selection Bias. Economists point out that if a number of firms are bidding on an oil exploration site, then the one who bids the most, and wins, probably spent more than it’s worth. Conversely, if you chose the cheapest bid on a construction job, the firm that wins the contract is more likely to have underestimated the true cost, and may go bankrupt trying to fulfill it. This is related to sin 11, as discussed in Chapter 20 .

13. Believing there are only twelve deadly sins. The thirteenth of the Seven Deadly Sins is being lulled into a sense of complacency, thinking you now know all of the insidious effects of averaging.