You probably didn’t set out to be a number-cruncher. Most people don’t. Most are people-persons who spend a great deal of time learning about people, honing their communication skills, and developing intervention strategies. Many don’t like math, avoid working with numbers, and never want to do research for a living.

Yet people and numbers seem to go together. The more people there are, the more someone has to keep track of who they are, where they come from, and where they are headed. People, whether customers, students or patients, come with test scores, evaluations, and outcome measures. The more you work with people, the more data there is that has to be summarized, understood, interpreted and communicated.

Fortunately, measuring skills don’t have to be acquired, just honed. We come packaged at birth with tremendous processing ability. We do it all the time, and with very little effort. When we walk, our brains automatically calculate the slope and curve of the path. We scan for obstructions and estimate the amount of effort needed to climb a hill or jump a curb.

When we listen, we calculate frequency, distance, and quality of tone. We can quickly detect the difference between children who are playing and those who are fighting When we meet people, our internal measuring systems automatically give us data. Before we even realize it, our predefined questions already have been asked and the data collected. Are they taller than us? Better dressed? Younger, older, or about the same? Would we like being friends; would they like us?

Measuring people is simply a matter of applying the principles we already use to a set of numbers. The only difference is that we must use a more indirect system. Instead of looking directly at a person, we look at numbers which describe that person.

Clearly, our indirect system is not exact. People are not numbers, nor can they be completed described with numbers. There are gaps in our knowledge, holes in our ability to understand. Our measurements are imprecise approximations and subject to errors in data collection, storage, retrieval and interpretation.

To counter the imperfections of our data, we generally restrict our focus to a group of people. Measurement is like a telescope than a microscope. We do best with a general look. The more detail we try to see, the more errors we make. We're good at broad long-distance generalizations; fairly good at medium levels of specificity, and terrible at close-up inspections.

If we were studying cars, we'd be very good detecting patterns of traffic, fair at understanding a fleet of cars, and terrible at being auto mechanics. In other words, we would be very good at describing an apple orchard, fair at describing a basket of apples, and poor at understanding a single apple. We can clearly see the mountains on the horizon, we can drive without running into things, but our close-up vision is so poor that we can barely make out the numbers on our cell phone. We can....you get the idea.