Here is an example of how looking at observable data on aging - such as mortality rates - can inform us of the nature of underlying mechanisms of aging: "What do you think are the odds that you will die during the next year? Try to put a number to it - 1 in 100? 1 in 10,000? Whatever it is, it will be twice as large 8 years from now. This startling fact was first noticed by the British actuary Benjamin Gompertz in 1825 and is now called the 'Gompertz Law of human mortality.' Your probability of dying during a given year doubles every 8 years. For me, a 25-year-old American, the probability of dying during the next year is a fairly miniscule 0.03% - about 1 in 3,000. When I'm 33 it will be about 1 in 1,500, when I'm 42 it will be about 1 in 750, and so on. By the time I reach age 100 (and I do plan on it) the probability of living to 101 will only be about 50%. This is seriously fast growth - my mortality rate is increasing exponentially with age. ... There is one important lesson, however, to be learned from Benjamin Gompertz's mysterious observation. By looking at theories of human mortality that are clearly wrong, we can deduce that our fast-rising mortality is not the result of a dangerous environment, but of a body that has a built-in expiration date." You might also look at the reliability theory of aging for a similar process of insight.