My last birthday wasn’t one of those difficult ages that end with a zero. Thank goodness! However, the passage of another year got me thinking. At that point, I told myself that age is just a number. Can you do a mental double-take? I think I did one. Can a statistician say that age is just a number? After all, it’s through numbers that statisticians understand the world and how it works.
After some pondering, I concluded that, yes, age is just a number! If anyone asks, tell them that’s the conclusion of a statistician.
You’re probably wondering how I came to this finding. I don’t think I’m deceiving myself just to feel better. In statistics, if you can’t trust your data, you can’t trust the results. Garbage data goes in and garbage results come out. This idea applies to all areas of statistical analysis including hypothesis tests, ANOVA, and regression.
But, wait a minute. Surely it’s an easy matter to calculate your age. Of course, it is. I have no doubt that my age represents the correct number of Earth orbits around the sun since I was born. Trust me; I’ve tried to find ways of coming up with smaller numbers, but nothing works!
In this post, I’ll walk you through the question of age and use a statistical perspective to show you why you shouldn’t put much stock in this number.
Is Age a Trustworthy Number?
Numbers can deceive you in a variety of ways. In statistics, we are interested in both the reliability and the validity of data. Are the measurements consistent and do they measure what you think they are measuring?
Reliability refers to the repeatability of measurements and experimental results. If you measure something several times, do you obtain the same measurements? Do similar studies tend to produce consistent results? Regarding my age, we’re good. At least we’re good in terms of reliability. Whenever I check the calendar, I’m the same age and no younger.
Validity refers to how well a conclusion satisfies the rigors of the scientific method, which includes a variety of different ideas. Consequently, there are different types of validity including internal, external and construct. For this post, I’ll focus on internal validity, which relates to confidence in the cause-and-effect relationships that we’re studying. It is here where I think age has problems as a measure.
Developing a Hypothetical Age Study
Before we proceed, we need to determine what we are studying. Variables aren’t good or bad in a vacuum. Before we can assess a variable, like age, we need to understand the context in which we’ll use it.
I’ll assume that we have some reason for why we don’t like higher ages. Given the dislike, it’s safe to assume that people imagine a correlation between age and adverse outcomes. As you grow older, you presumably expect more unpleasant things to happen. That explains why people want a younger age. But, which outcomes? That’s not entirely clear as we mourn the passage of another year. For a proper study, we need to define these. Also, we need to assess not only whether there is a correlation between age and negative outcomes but whether age truly causes these outcomes.
All of these issues suggest that our hypothetical age study falls short in terms of internal validity. We’re not sure which outcomes we’re studying, and we can’t be sure of causal relationships. So far we don’t have much reason to trust our conclusion that older ages cause negative outcomes.
Defining Age-Related Outcomes
Let’s see if we can add some clarity by defining outcomes. For my initial assessment, it appears that there are two broad categories of life outcomes that we can use in our hypothetical age study.
Biological changes: It’s true that as we age, natural processes occur and we change. For example, we start as infants, go through puberty, mental and physical abilities increase and then decline, and so on. These changes are unavoidable, but they happen at different rates for different people. We can affect some of the underlying factors. For instance, we can eat healthier, exercise, reduce UV exposure, etc. However, some of the underlying variables are not controllable, such as genetics.
For our hypothetical study, we need to know which way the causal arrow points. Does the increasing calendar age cause the biological changes? Or, do these changes define our concept of age? I’d argue that the causality runs in this order: Underlying variables -> Biological changes -> Concept of age.
Important life affairs: These outcomes are not biological processes and are, therefore, less dependent on the passage of time. These outcomes include happiness, maximizing your potential, developing strong relationships, performing gratifying activities, and making positive contributions to your community. For assessing the quality of life, these seem much more important.
Where does this leave us? For biological changes, causality appears to run counter to the theoretical underpinnings of our study. In other words, it might be that biological changes create our concept of age rather age causing negative outcomes.
The important life affairs category doesn’t seem to depend on age at all, and these are the most important life outcomes. You can use these outcomes to define a life as being well lived.
Conclusions About Using Age as a Measure
None of this fits our thought experiment hypothesis that age causes bad things to happen. Based on these issues, my opinion as a statistician is that age does not have a causal relationship with the important outcomes in our thought experiment. In other words, age is just a number!
Let this conclusion free you from worrying about your age increasing every year. Don’t sweat that number. However, this finding places responsibility firmly on you for making smart choices. The choices you make have a greater impact on your life than a meaningless number. And, the rest you can’t control.