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Logical Fallacies

Conditional Life Expectancy Fallacy

The fallacy of confusing unconditional life expectancy (at birth) with conditional life expectancy (given you have reached a certain age), leading to incorrect planning and predictions.

THE IDEA

You Should Be Dead by Now

This brings to mind another common violation of probability by prime-time TV financial experts, who may be selected for their looks, their charisma, and their presentation skills, but certainly not for their incisive minds. For instance, a fallacy that I saw commonly made by a prominent TV financial guru goes as follows: “The average American is expected to live seventy-three years. Therefore if you are sixty-eight you can expect to live five more years, and should plan accordingly.” She went into precise prescriptions of how the person should invest for a five-more-years horizon. Now what if you are eighty? Is your life expectancy minus seven years? What these journalists confuse is the unconditional and conditional life expectancy. At birth, your unconditional life expectancy may be seventy-three years. But as you advance in age and do not die, your life expectancy increases along with your life. Why? Because other people, by dying, have taken your spot in the statistics, for expectation means average. So if you are seventy-three and are in good health, you may still have, say, nine years in expectation. But the expectation would change, and at eighty-two, you will have another five years, provided of course you are still alive. Even someone one hundred years old still has a positive conditional life expectation. Such a statement, when one thinks about it, is not too different from the one that says: Our operation has a mortality rate of 1%. So far we have operated on ninety-nine patients with great success; you are our one hundredth, hence you have a 100% probability of dying on the table.

When to use it

  • Financial planning: Adjusting retirement savings and withdrawal strategies based on conditional life expectancy at retirement age.

  • Medical prognosis: Estimating remaining years of life for a patient with a chronic condition, considering age and health status.

  • Insurance: Setting premiums for life or health insurance that account for conditional probabilities at different ages.

  • Risk assessment: Evaluating the likelihood of an event after some conditions are met, such as survival after a given age or treatment success after initial steps.

FROM THEORY TO EVERYDAY LIFE

In practice

01

TV Financial Guru's Advice

A prominent TV financial guru advises a 68-year-old to plan for a 5-year life expectancy based on the average life expectancy of 73 years, incorrectly assuming unconditional life expectancy applies regardless of current age.

02

Surgery Mortality Misinterpretation

A surgery with a 1% mortality rate successfully operates on 99 patients. A layperson claims the 100th patient has a 100% chance of dying, confusing the unconditional rate with the conditional probability given past events.

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A MOMENT TO REFLECT

Put it to work.

  1. How does your life expectancy change as you age, and why is it important to use conditional rather than unconditional expectations?

  2. In what other areas of life do people commonly confuse unconditional and conditional probabilities?

  3. Can you think of a personal decision where relying on unconditional statistics could lead to poor planning?

CONTINUE THE CONVERSATION

Source texts

  • Fooled by Randomness The Hidden Role of Chance in the Markets and in Life (Nassim Nicholas Taleb)
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