If you’re a people-person who is facing statistics for the first time, this is the book for you. The math won’t be hard, we’ll go step by step, and I won’t make you memorize formulas. I think statistics is scary when you face it alone. So let’s do it together.
The funny thing about being a people-person: the more people you meet, the more numbers you collect. There are test scores, evaluations, key performance indicators, and…more paperwork. People have to be supervised and coordinated. And the numbers that describe them have to be collected, understand, summarized and reported. Where there are people, you’ll find numbers.
Fortunately, we are born with great measuring skills. We are natural collectors of information. We sense sights, sounds and smells. Our brains automatically calculate how far we are from the car ahead of us, how deep the potholes are, and how fast the bicyclist is going. We calculate slopes, shapes, shades and angles. We make thousands of decisions; all based on data we collect. In our everyday lives, we are scientists.
In statistics, we are going to hone the skills we already possess. In particular, we are going to use numbers to describe people. It’s a fairly indirect approach but highly useful. The indirectness means, of course, that we’ll make mistakes. But the usefulness of the system makes up for that problem.
Think of the difference between maps and the real world. Maps are representations of the real thing. They are subject to error (ever have a set of directions lead you astray?). But they are highly portable and useful. Numbers are the same way for people. We shouldn’t confuse the score on an intelligence test with intelligence. Just as maps are the reality, scores aren’t the reality. But representations of reality can be helpful.
Research focuses on group data. This is both an advantage and disadvantage. On the good side, we can see the broad picture. We can use group data to make generalizations, heuristics and rules that often work for most people. On the bad side, looking at a group means we will miss some individuals. It’s like coming to the conclusion that peanuts can safely to added to a school lunch. As a group generalization, it’s true. Most people can eat peanuts and benefit from doing so. But such a finding will be fatal to some children.
So remember that group data is helpful for a generalization. Statistics is a great tool for general surveying of vast wildernesses, not so good at telling what’s over the next hill, and really rotten at being able to tell whether you will fall into quicksand. So statistics looks at patterns, trends, and things that are typically true.
We often use statistics to describe things. Descriptive statistics tries to reduce a large pile of data into as few numbers as it possibly can. The ultimate goal is to find a single number that describes a whole group. In contrast, inferential statistics is when we use data as the basis for estimates, predictions and guesses. Descriptive stats is telling you that it’s raining right now. Inferential stats tries to figure out if it will rain tomorrow.
This covers all the important things you need to know about statistics. Click on this image to order it on Amazon ($18.95). NOTE: CURRENTLY OUT OF PRINT.
Or get the e-version free when you take my course: A 10 Day Guided Tour of Statistics (in process).