Chapter 1: Stats Starts here
TEKS: 2D, 3A, 3B, 4B
TEKS: 2D, 3A, 3B, 4B
Welcome to the World of Statistics
In this first lesson you will get a big picture idea of what statistics is all about. It's a lot more than just finding the mean, median, and mode. This lesson is filled with a lot of vocabulary as well, so make sure you know your vocab words and if you need to review them, visit the Statistics Dictionary.
What You Should Learn
1. The 5 W's
2. The most important W's that must be answered in order to provide context.
3. The difference between Categorical and Quantitative Variables.
What Are Data?
Statistics is the science of collecting, organizing, analyzing, and interpreting data in order to make decisions.
Data consist of information coming from observations, counts, measurements, or responses. (Single value = datum, while data is the collect of values)
In other words, data is the information that you collect and statistics is what you do with that information.
But data values, no matter what kind, are useless without their context. In order to provide context to your data you should establish "The 5 W's": Who, What, When, Where, and Why. Sometimes How is added to the list as well. Most importantly, if you can't answer the Who and What then you don't have data and you don't have any useful information.
Who?
The following vocabulary pertains to answering the question "Who" for your data.
Case - A case is an individual about whom or which we have data.
Respondent - A respondent is an individual who answers a survey.
Subject - People on whom we experiment are subjects. More specifically, people in an experiment are called participants, but animals, plants, Web sites, and other inanimate subjects are often called experimental units.
Often, the cases are a sample selected from some larger population that we'd like to understand.
What and Why?
The following vocabulary pertains to answering the questions "What & Why" for your data.
Variables - The characteristics recorded about each individual are called variables.
Categorical Variable (Qualitative Variable) - A categorical variable consists of attributes, labels, and nonnumerical data.
Quantitative Variable - A quantitative variable consists of numerical measurements or counts.
Numbers That Aren't Quantitative
Don't assume that every data value that is a number is considered a quantitative variable. For example, what's your student ID number? Whatever it is, we can see that it's a number, but is it a quantitative variable? The answer is "no", because it doesn't have units. Is it a categorical variable? Yes, because it identifies you.
This is why we must ask "The 5 W's" in order to establish context for data. Some other numbers that could be categorical variables, include Social Security Numbers, Jersey Numbers, Area Codes, and Tracking Numbers, all of which identify something about a subject.
Where, When and How?
The more we know about the data, the more we'll understand about the world. If possible, we'd like to know the When and Where of data as well. Values recorded hundreds of years ago may mean something different than similar values recorded today. Likewise, values measured in a different countries may differ in meaning as well. How the data are collected is probably one of the most important as it can make the difference between insight and nonsense. One primary concern of Statistics is designing methods for collecting meaningful data.
Throughout this course, we'll often provide a note listing the W's (and H) of the data. It's a recommended habit. The first step of any data analysis is to know why you are examining the data, whom each row of your data refers, and what the variables record.
We will have a Check for Understanding tomorrow (Friday).