Statistics For Dummies

Deborah J. Rumsey

• Éditeur: For Dummies
• Année: 2016
• ISBN:
  9781119297512
• Édition: 2
• Sujet: Mathematics
• Sous-sujet: Probability & Statistics

The fun and easy way to get down to business with statistics

Stymied by statistics? No fear? this friendly guide offers clear, practical explanations of statistical ideas, techniques, formulas, and calculations, with lots of examples that show you how these concepts apply to your everyday life.

Statistics For Dummies shows you how to interpret and critique graphs and charts, determine the odds with probability, guesstimate with confidence using confidence intervals, set up and carry out a hypothesis test, compute statistical formulas, and more.

• Tracks to a typical first semester statistics course
• Updated examples resonate with today's students
• Explanations mirror teaching methods and classroom protocol

Packed with practical advice and real-world problems, Statistics For Dummies gives you everything you need to analyze and interpret data for improved classroom or on-the-job performance.

Part 1

Vital Statistics about Statistics

IN THIS PART …

When you turn on the TV or open a newspaper, you’re bombarded with numbers, charts, graphs, and statistical results. From today’s poll to the latest major medical breakthroughs, the numbers just keep coming. Yet much of the statistical information you’re asked to consume is actually wrong — by accident or even by design. How is a person to know what to believe? By doing a lot of good detective work.

This part helps awaken the statistical sleuth that lies within you by exploring how statistics affect your everyday life and your job, how bad much of the information out there really is, and what you can do about it. This part also helps you get up to speed with some useful statistical jargon.

Chapter 1

Statistics in a Nutshell

IN THIS CHAPTER

Finding out what the process of statistics is all about

Gaining success with statistics in your everyday life, your career, and in the classroom

The world today is overflowing with data to the point where anyone (even me!) can be overwhelmed. I wouldn’t blame you if you were cynical right now about statistics you read about in the media — I am too at times. The good news is that while a great deal of misleading and incorrect information is lying out there waiting for you, a lot of great stuff is also being produced; for example, many studies and techniques involving data are helping improve the quality of our lives. Your job is to be able to sort out the good from the bad and be confident in your ability to do that. Through a strong understanding of statistics and statistical procedures, you gain power and confidence with numbers in your everyday life, in your job, and in the classroom. That’s what this book is all about.

In this chapter, I give you an overview of the role statistics plays in today’s data-packed society and what you can do to not only survive but thrive. You get a much broader view of statistics as a partner in the scientific method — designing effective studies, collecting good data, organizing and analyzing the information, interpreting the results, and making appropriate conclusions. (And you thought statistics was just number-crunching!)

Thriving in a Statistical World

It’s hard to get a handle on the flood of statistics that affect your daily life in large and small ways. It begins the moment you wake up in the morning and check the news and listen to the meteorologist give you her predictions for the weather based on her statistical analyses of past data and present weather conditions. You pore over nutritional information on the side of your cereal box while you eat breakfast. At work you pull numbers from charts and tables, enter data into spreadsheets, run diagnostics, take measurements, perform calculations, estimate expenses, make decisions using statistical baselines, and order inventory based on past sales data.

At lunch you go to the No. 1 restaurant based on a survey of 500 people. You eat food that was priced based on marketing data. You go to your doctor’s appointment where they take your blood pressure, temperature, weight, and do a blood test; after all the information is collected, you get a report showing your numbers and how you compare to the statistical norms.

You head home in your car that’s been serviced by a computer running statistical diagnostics. When you get home, you turn on the news and hear the latest crime statistics, see how the stock market performed, and discover how many people visited the zoo last week.

At night, you brush your teeth with toothpaste that’s been statistically proven to fight cavities, read a few pages of your New York Times Best-Seller (based on statistical sales estimates), and go to sleep — only to start it all over again the next morning. But how can you be sure that all those statistics you encounter and depend on each day are correct? In Chapter 2, I discuss in more depth a few examples of how statistics is involved in our lives and workplaces, what its impact is, and how you can raise your awareness of it.

Some statistics are vague, inappropriate, or just plain wrong. You need to become more aware of the statistics you encounter each day and train your mind to stop and say “wait a minute!”, sift through the information, ask questions, and raise red flags when something’s not quite right. In Chapter 3, you see ways in which you can be misled by bad statistics and develop skills to think critically and identify problems before automatically believing results.

Like any other field, statistics has its own set of jargon, and I outline and explain some of the most commonly used statistical terms in Chapter 4. Knowing the language increases your ability to understand and communicate statistics at a higher level without being intimidated. It raises your credibility when you use precise terms to describe what’s wrong with a statistical result (and why). And your presentations involving statistical tables, graphs, charts, and analyses will be informational and effective. (Heck, if nothing else, you need the jargon because I use it throughout this book; don’t worry though, I always review it.)

In the next sections, you see how statistics is involved in each phase of the scientific method.

Designing Appropriate Studies

Everyone’s asking questions, from drug companies to biologists; from marketing analysts to the U.S. government. And ultimately, everyone will use statistics to help them answer their questions. In particular, many medical and psychological studies are done because someone wants to know the answer to a question. For example,

• Will this vaccine be effective in preventing the flu?
• What do Americans think about the state of the economy?
• Does an increase in the use of social networking Web sites cause depression in teenagers?

The first step after a research question has been formed is to design an effective study to collect data that will help answer that question. This step amounts to figuring out what process you’ll use to get the data you need. In this section, I give an overview of the two major types of studies — surveys and experiments — and explore why it’s so important to evaluate how a study was designed before you believe the results.

Surveys

An observational study is one in which data is collected on individuals in a way that doesn’t affect them. The most common observational study is the survey. Surveys are questionnaires that are presented to individuals who have been selected from a population of interest. Surveys take many different forms: paper surveys sent through the mail, questionnaires on Web sites, call-in polls conducted by TV networks, phone surveys, and so on.

If conducted properly, surveys can be very useful tools for getting information. However, if not conducted properly, surveys can result in bogus information. Some problems include improper wording of questions, which can be misleading, lack of response by people who were selected to participate, or failure to include an entire group of the population. These potential problems mean a survey has to be well thought out before it’s given.

Many researchers spend a great deal of time and money to do good surveys, and you’ll know (by the criteria I discuss in Chapter 16) that you can trust them. However, as you are besieged with so many different types of surveys found in the media, in the workplace, and in many of your classes, you need to be able to quickly examine and critique how a survey was designed and conducted and be able to point out specific problems in a well-informed way. The tools you need for sorting through surveys are found in Chapter 16.

Experiments

An experiment imposes one or more treatments on the participants in such a way that clear comparisons can be made. After the treatments are applied, the responses are recorded. For example, to study the effect of drug dosage on blood pressure, one group may take 10 mg of the drug, and another group may take 20 mg. Typically, a control group is also involved, in which subjects each receive a fake treatment (a sugar pill, for example), or a standard, nonexperimental treatment (like the existing drugs given to AIDS patients.)

Good and credible experiments are designed to minimize bias, collect lots of good data, and make appropriate comparisons (treatment group versus control group). Some potential problems that occur with experiments include researchers and/or subjects who know which treatment they got, factors not controlled for in the study that affect the outcome (such as weight of the subject when studying drug dosage), or lack of a control group (leaving no baseline to compare the results with).

But when designed correctly, an experiment can help a researcher establish a cause-and-effect relationship if the difference in responses between the treatment group and th...