Read Online Statistics for Dummies by Deborah Jean Rumsey - Free Book Online
Authors: Deborah Jean Rumsey
Tags: Reference, Non-Fiction
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opinions on whether they're spending too much time on the Internet. If you send out a survey over e-mail, your results won't represent the opinions of all teenagers , which is your intended population. They will represent only those teenagers who have Internet access. Does this sort of statistical mismatch happen often? You bet.
HEADS UP
One of the biggest culprits of statistical misrepresentation caused by bad sampling is surveys done on the Internet. You can find thousands of examples of surveys on the Internet that are done by having people log on to a particular Web site and give their opinions. Even if 50,000 people in the United States complete a survey on the Internet, it doesn't represent the population of all Americans. It represents only those folks who have Internet access, who logged on to that particular Web site, and who were interested enough to participate in the survey (which typically means that they have strong opinions about the topic in question).
REMEMBER
The next time you're hit with the results of a study, find out the makeup of the sample of participants and ask yourself whether this sample represents the intended population. Be wary of any conclusions being made about a broader population than what was actually studied. (More in Chapter 16 .)
Random
A random sample is a good thing; it gives every member of the population an equal chance of being selected, and it uses some mechanism of chance to choose them. What this really means is that people don't select themselves to participate, and no one in the population is favored over another individual in the selection process.
As an example of how the experts do it, here is the way The Gallup Organization does its random sampling process. It starts with a computerized list of all telephone exchanges in America, along with estimates of the number of residential households that have those exchanges. The computeruses a procedure called random digit dialing (RDD) to randomly create phone numbers from those exchanges, and then selects samples of telephone numbers from those. So what really happens is that the computer creates a list of all possible household phone numbers in America, and then selects a subset of numbers from that list for Gallup to call. (Note that some of these phone numbers may not yet be assigned to a household, creating some logistical issues to deal with.)
Another example of random sampling involves the manufacturing sector and the concept of quality control. Most manufacturers have strict specifications for their products being produced, and errors in the process can cost them money, time, and credibility. Many companies try to head off problems before they get too large by monitoring their processes and using statistics to make decisions as to whether the process is operating correctly or needs to be stopped. For more on quality control and statistics, see Chapter 19 .
Examples of non-random (in other words bad ) sampling include samples from polls for which you phone in your opinion. This is not truly a random sample because it doesn't give everyone in the population an equal opportunity to participate in the survey. (If you have to buy the newspaper or watch that TV show, and then agree to write in or call in, that gives you a big clue that the sampling process is not random.) For more on sampling and polls, see Chapter 16 .
REMEMBER
Any time you look at results of a study that were based on a sample of individuals, read the fine print, and look for the term "random sample." If you see that term, dig further into the fine print to see how the sample was actually selected and use the preceding definition to verify that the sample was, in fact, selected randomly.
Bias
Bias is a word you hear all the time, and you probably know that it means something bad. But what really constitutes bias? Bias is systematic favoritism that is present in the data collection process, resulting in lopsided, misleading results.
Bias can occur
HEADS UP
One of the biggest culprits of statistical misrepresentation caused by bad sampling is surveys done on the Internet. You can find thousands of examples of surveys on the Internet that are done by having people log on to a particular Web site and give their opinions. Even if 50,000 people in the United States complete a survey on the Internet, it doesn't represent the population of all Americans. It represents only those folks who have Internet access, who logged on to that particular Web site, and who were interested enough to participate in the survey (which typically means that they have strong opinions about the topic in question).
REMEMBER
The next time you're hit with the results of a study, find out the makeup of the sample of participants and ask yourself whether this sample represents the intended population. Be wary of any conclusions being made about a broader population than what was actually studied. (More in Chapter 16 .)
Random
A random sample is a good thing; it gives every member of the population an equal chance of being selected, and it uses some mechanism of chance to choose them. What this really means is that people don't select themselves to participate, and no one in the population is favored over another individual in the selection process.
As an example of how the experts do it, here is the way The Gallup Organization does its random sampling process. It starts with a computerized list of all telephone exchanges in America, along with estimates of the number of residential households that have those exchanges. The computeruses a procedure called random digit dialing (RDD) to randomly create phone numbers from those exchanges, and then selects samples of telephone numbers from those. So what really happens is that the computer creates a list of all possible household phone numbers in America, and then selects a subset of numbers from that list for Gallup to call. (Note that some of these phone numbers may not yet be assigned to a household, creating some logistical issues to deal with.)
Another example of random sampling involves the manufacturing sector and the concept of quality control. Most manufacturers have strict specifications for their products being produced, and errors in the process can cost them money, time, and credibility. Many companies try to head off problems before they get too large by monitoring their processes and using statistics to make decisions as to whether the process is operating correctly or needs to be stopped. For more on quality control and statistics, see Chapter 19 .
Examples of non-random (in other words bad ) sampling include samples from polls for which you phone in your opinion. This is not truly a random sample because it doesn't give everyone in the population an equal opportunity to participate in the survey. (If you have to buy the newspaper or watch that TV show, and then agree to write in or call in, that gives you a big clue that the sampling process is not random.) For more on sampling and polls, see Chapter 16 .
REMEMBER
Any time you look at results of a study that were based on a sample of individuals, read the fine print, and look for the term "random sample." If you see that term, dig further into the fine print to see how the sample was actually selected and use the preceding definition to verify that the sample was, in fact, selected randomly.
Bias
Bias is a word you hear all the time, and you probably know that it means something bad. But what really constitutes bias? Bias is systematic favoritism that is present in the data collection process, resulting in lopsided, misleading results.
Bias can occur
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