## Description

## Statistics for People Who Think They Hate Statistics 5th edition eBook

Statistics or Sadistics?

It’s Up to You

Difficulty Scale (really easy)

WHAT YOU’LL LEARN ABOUT IN THIS

CHAPTER

What statistics is all about

Why you should take statistics

How to succeed in this course

WHY STATISTICS?

You’ve heard it all before, right? “Statistics is difficult,” “The

math involved is impossible,” “I don’t know how to use a

computer,” “What do I need this stuff for?” “What do I do

next?” and the famous cry of the introductory statistics student,

“I don’t get it!”

Author

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Well, relax. Students who study introductory statistics find

themselves, at one time or another, thinking about at least one

of the preceding, if not actually sharing it with another student,

their spouse, a colleague, or a friend.

Video 1.1

And all kidding aside, some statistics courses can easily be

described as sadistics. That’s because the books are

repetitiously boring and the authors have no imagination.

That’s not the case for you. The fact that you or your

instructor has selected Statistics for People Who (Think They)

Hate Statistics shows that you’re ready to take the right

approach: one that is unintimidating, informative, and applied

(and even a little fun) and that tries to teach you what you need

to know about using statistics as the valuable tool that it is.

If you’re using this book in a class, it also means that your

instructor is clearly on your side—he or she knows that

statistics can be intimidating but has taken steps to see that it is

not intimidating for you. As a matter of fact, we’ll bet there’s a

good chance (as hard as it may be to believe) that you’ll be

enjoying this class in just a few short weeks.

A 5-MINUTE HISTORY OF STATISTICS

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Before you read any further, it would be useful to have some

historical perspective about this topic called statistics. After all,

almost every undergraduate in the social, behavioral, and

biological sciences and every graduate student in education,

nursing, psychology, social welfare and social services, and

anthropology (you get the picture) is required to take this

course. Wouldn’t it be nice to have some idea from whence the

topic it covers came? Of course it would.

Way, way back, as soon as humans realized that counting was

a good idea (as in “How many of these do you need to trade for

one of those?”), collecting information also became a useful

skill. If counting counted, then one would know how many

times the sun would rise in one season, how much food was

needed to last the winter, and what amount of resources

belonged to whom.

That was just the beginning. Once numbers became part of

language, it seemed like the next step was to attach these

numbers to outcomes. That started in earnest during the 17th

century, when the first set of data pertaining to populations was

collected. From that point on, scientists (mostly

mathematicians but then physical and biological scientists as

well) needed to develop specific tools to answer specific

questions. For example, Francis Galton (a cousin of Charles

Darwin, by the way), who lived from 1822 to 1911, was very

interested in the nature of human intelligence. To explore one

of his primary questions regarding the similarity of intelligence

among family members, he used a specific statistical tool

called the correlation coefficient (first developed by

mathematicians), and then he popularized its use in the

behavioral and social sciences. You’ll learn all about this tool

in Chapter 5.

In fact, most of the basic statistical procedures that you will

learn about were first developed and used in the fields of

agriculture, astronomy, and even politics. Their application to

human behavior came much later.

The past 100 years have seen great strides in the invention of

new ways to use old ideas. The simplest test for examining the

differences between the averages of two groups was first

advanced during the early 20th century. Techniques that build

on this idea were offered decades later and have been greatly

refined. And the introduction of personal computers and such

programs as SPSS® (now made by an IBM company; see

Appendix A, available online at www.sagepub.com/salkind5e)

and Excel (from Microsoft) have opened up the use of

sophisticated techniques to anyone who wants to explore these

fascinating topics.

The introduction of powerful personal computers has been

both good and bad. It’s good because most statistical analyses

no longer require access to a huge and expensive mainframe

computer. Instead, a simple personal computer costing less than

$500 can do 95% of what 95% of the people need. On the other

hand, less than adequately educated students (such as your

fellow students who passed on taking this course!) will take any

old data they have and think that by running them through some

sophisticated SPSS or Excel analysis, they will have reliable,

trustworthy, and meaningful outcomes—not true. What your

professor would say is, “Garbage in, garbage out.” In other

words, if you don’t start with reliable and trustworthy data,

what you’ll have after your data are analyzed are unreliable and

untrustworthy results.

Today, statisticians in all different areas, from criminal

justice to geophysics to psychology, find themselves using

basically the same techniques to answer different questions.

There are, of course, important differences in how data are

collected, but for the most part, the analyses (the plural of

analysis) that are done following the collection of data (the

plural of datum) tend to be very similar, even if they are called

different things. The moral here? This class will provide you

with the tools to understand how statistics are used in almost

any discipline. Pretty neat, and all for just three or four credits.

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If you want to learn more about the history of statistics and

see a historical time line, a great place to start is at Saint

Anselm’s College website at

http://www.anselm.edu/homepage/jpitocch/biostatshist.html

and the University of California at Los Angeles at

http://www.stat.ucla.edu/history/. You’ll find tons of good stuff

at both places.

STATISTICS: WHAT IT IS (AND ISN’T)

Statistics for People Who (Think They) Hate Statistics is a book

about basic statistics and how to apply them to a variety of

different situations, including the analysis and understanding of

information.

In the most general sense, statistics describes a set of tools

and techniques that is used for describing, organizing, and

interpreting information or data. Those data might be the scores

on a test taken by students participating in a special math

curriculum, the speed with which problems are solved, the

number of patient complaints when health care providers use

one type of drug rather than another, the number of errors in

each inning of a World Series game, or the average price of a

dinner in an upscale restaurant in Sante Fe, New Mexico.

Author

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In all of these examples, and the million more we could think

of, data are collected, organized, summarized, and then

interpreted. In this book, you’ll learn about collecting,

organizing, and summarizing data as part of descriptive

statistics. And then you’ll learn about interpreting data when

you learn about the usefulness of inferential statistics.

What Are Descriptive Statistics?

Descriptive statistics are used to organize and describe the

characteristics of a collection of data. The collection is

sometimes called a data set or just data.

For example, the following list shows you the names of 22

college students, their major areas of study, and their ages. If

you needed to describe what the most popular college major is,

you could use a descriptive statistic that summarizes their

choice (called the mode). And if you wanted to know the

average age, you could easily compute another descriptive

statistic that identifies this variable (that one’s called the

mean). Both of these simple descriptive statistics are used to

describe data. They do a fine job of allowing us to represent the

characteristics of a large collection of data such as the 22 cases

in our example.

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So watch how simple this is. To find the most frequently

selected major, just find the one that occurs most often. And to

find the average age, just add up all the age values and divide

by 22. You’re right—the most often occurring major is

psychology (9 times) and the average age is 20.3 years. Look,

Ma! No hands—you’re a statistician.

What Are Inferential Statistics?

Inferential statistics are often (but not always) the next step

after you have collected and summarized data. Inferential

statistics are used to make inferences from a smaller group of

data (such as our group of 22 students) to a possibly larger one

(such as all the undergraduate students in the College of Arts

and Sciences). Sometimes, it’s just fine to describe outcomes

only; other times, the next step—using inferential statistics—is

needed.

This smaller group of data is often called a sample, which is

a portion, or a subset, of a population. For example, all the fifth

graders in Newark, New Jersey, would be a population (all the

occurrences with certain characteristics—being in fifth grade

and living in Newark), whereas a selection of 150 of these

students would be a sample.

Let’s look at another example. Your marketing agency asks

you (a newly hired researcher) to determine which of several

names is most appealing for a new brand of potato chip. Will it

be Chipsters? FunChips? Crunchies? As a statistics pro (we

know we’re moving a bit ahead of ourselves, but keep the

faith), you need to find a small group of potato chip eaters that

is representative of all potato chip fans and ask these munchers

to tell you which one of the three names they like the most.

Then, if you did things right, you can easily apply the findings

to the huge group of potato chip eaters.

Or, let’s say you’re interested in the best treatment for a

particular type of disease. Perhaps you’ll try a new drug as one

alternative, a placebo (or a substance that is known not to have

any effect) as another alternative, and simply nothing as the

third alternative to see what happens. Well, you find out that a

larger number of patients get better when no action is taken and

nature just takes its course! The drug does not have any effect.

Then, with that information, you infer that the same thing is

true for the larger group of patients who suffer from the

disease, given the results of your experiment.

In Other Words . . .

Statistics is a tool that helps us understand the world around us.

It does so by organizing information we’ve collected and then

letting us make certain statements about how characteristics of

those data are applicable to new settings. Descriptive and

inferential statistics work hand in hand, and which one you use

and when depends on the question you want answered.

WHAT AM I DOING IN A STATISTICS CLASS?

There are probably many reasons why you find yourself using

this book. You might be enrolled in an introductory statistics

class. Or you might be reviewing for your comprehensive

exams. Or you might even be reading this on summer vacation

(horrors!) in preparation and review for a more advanced class.

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In any case—whether you have to take a final exam at the

end of a formal course, you’re just in it of your own accord, or

you’re taking the course online 500 miles from the instructor—

you are a statistics student.

And there are plenty of good reasons to be studying this

material—some fun, some serious, and some both. Here’s the

list of some of the things that my students hear at the beginning

of our introductory statistics course.

1. Statistics 101 or Statistics 1 or whatever it’s called at

your school looks great listed on your transcript. Kidding

aside, this may be a required course for you to complete

your major. But even if it is not, having these skills is

definitely a big plus when it comes time to apply for a job

or for further schooling. And with more advanced

courses, your résumé will be even more impressive. In

tough job markets, an edge like this is very important.

2. If this is not a required course, taking basic statistics sets

you apart from those students who do not take it. It shows

that you are willing to undertake a course that is above

average in difficulty and commitment.

3. Basic statistics is an intellectual challenge of a kind that

you might not be used to. There’s a good deal of thinking

that’s required, a bit of math (but not lots, and for a tuneup,

see Appendix E), and some integration of ideas and

application. The bottom line is that all this activity adds

up to what can be an invigorating intellectual experience

because you learn about a whole new area or discipline.

4. There’s no question that having some background in

statistics makes you a better student in the social or

behavioral sciences, because you will have a better

understanding not only of what you read in journals but

also what your professors and colleagues may be

discussing and doing in and out of class. You will be

amazed the first time you say to yourself, “Wow, I

actually understand what they’re talking about.” And it

will happen over and over again because you will have

the basic tools necessary to understand exactly how

scientists reach the conclusions they do.

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5. If you plan to pursue a graduate degree in education,

anthropology, neuroscience, economics, nursing, urban

planning, sociology, or any one of many other social,

behavioral, and biological pursuits, this course will give

you the foundation you need to go further.

6. Finally, you can brag that you completed a course that

everyone thinks is the equivalent of building and running

a nuclear reactor.