Elementary Statistics Chapter 1

Definitions
Data are collections of observations, such as measurements, genders, or survey responses. (A single data value is called a
datum, a term that does not see very much use.)
Statistics is the science of planning studies and experiments; obtaining data; and then organizing, summarizing, presenting,
analyzing, and interpreting
those data and then drawing conclusions based on them.
A population is the complete collection of all measurements or data that are being considered.
A census is the collection of data from every member of the population.
A sample is a subcollection of members selected from a population.
Because populations are often very large, a common objective of the use of statistics is to obtain data from a sample and
then use those data to form a conclusion about the population. See Example 1.
Example 1 Gallup Poll: Identity Theft
In a poll conducted by the Gallup corporation, 1013 adults in the United States were randomly selected and surveyed about
identity theft. Results showed that 66% of the respondents worried about identity theft frequently or occasionally. Gallup
pollsters decided who would be asked to participate in the survey and they used a sound method of randomly selecting
adults. The respondents are not a voluntary response sample, and the results are likely to be better than those obtained
from the America OnLine survey discussed earlier. In this case, the population consists of all 241,472,385 adults in the
United States, and it is not practical to survey each of them. The sample consists of the 1013 adults who were surveyed.
The objective is to use the sample data as a basis for drawing a conclusion about the population of all adults, and methods
of statistics are helpful in drawing such conclusions.
Origin of “Statistics”The word statistics is derived from the Latin word status (meaning “state”). Early uses of statistics
involved compilations of data and graphs describing various aspects of a state or country. In 1662, John Graunt published
statistical information about births and deaths. Graunt's work was followed by studies of mortality and disease rates,
population sizes, incomes, and unemployment rates. Households, governments, and businesses rely heavily on statistical
data for guidance. For example, unemployment rates, inflation rates, consumer indexes, and birth and death rates are
carefully compiled on a regular basis, and the resulting data are used by business leaders to make decisions affecting future
hiring, production levels, and expansion into new markets.
1.2
Statistical and Critical Thinking
Key Concept This section provides an overview of the process involved in conducting a statistical study. This process
consists of “prepare, analyze, and conclude.” We begin with a preparation that involves consideration of the context,
consideration of the source of data, and consideration of the sampling method. Next, we construct suitable graphs, explore
the data, and execute computations required for the statistical method being used. Finally, we form conclusions by
determining whether results have statistical significance and practical significance. See Figure 12 for a summary of this
process.

Figure 1.2
Statistical Thinking
Figure 1 2 includes key elements in a statistical study. Note that the procedure outlined in Figure 12 does not focus on
mathematical calculations. Thanks to wonderful developments in technology, we now have tools that effectively do the
number crunching so that we can focus on understanding and interpreting results.
Prepare Context Let's consider the data in Table 11.
(The data are from Data Set 6 in Appendix B.) The data in Table 11consist of measured IQ scores and measured brain
volumes from 10 different subjects. The data are matched in the sense that each individual “IQ/brain volume” pair of values
is from the same subject. The first subject had a measured IQ score of 96 and a brain volume of 1005 cm 3. The format of
Table 11 suggests the following goal: Determine whether there is a relationship between IQ score and brain volume. This
goal suggests a possible hypothesis: People with larger brains tend to have higher IQ scores.

Source of the Data The data in Table 11 were provided by M. J. Tramo, W. C. Loftus, T. A. Stukel, J. B. Weaver, and M. S.
Gazziniga, who discuss the data in the article “Brain Size, Head Size, and IQ in Monozygotic Twins,” Neurology, Vol. 50. The
researchers are from reputable medical schools and hospitals, and they would not gain by putting spin on the results. In
contrast, Kiwi Brands, a maker of shoe polish, commissioned a study that resulted in this statement, which was printed in
some newspapers: “According to a nationwide survey of 250 hiring professionals, scuffed shoes was the most common
reason for a male job seeker's failure to make a good first impression.” We should be very wary of such a survey in which
the sponsor can somehow profit from the results. When physicians who conduct clinical experiments on the efficacy of drugs
receive funding from drug companies they have an incentive to obtain favorable results. Some professional journals, such as
the Journal of the American Medical Association, now require that physicians report such funding in journal articles. We
should be skeptical of studies from sources that may be biased.
Sampling Method The data in Table 11 were obtained from subjects who were recruited by researchers, and the subjects
were paid for their participation. All subjects were between 24 years and 43 years of age, they all had at least a high school
education, and the medical histories of subjects were reviewed in an effort to ensure that no subjects had neurologic or
psychiatric disease. In this case, the sampling method appears to be sound. Sampling methods and the use of
randomization will be discussed in Section 14, but for now, we simply emphasize that a sound sampling method is
absolutely essential for good results in a statistical study. It is generally a bad practice to use voluntary response (or
selfselected) samples, even though their use is common.
Value of a Statistical Life
The value of a statistical life (VSL) is a measure routinely calculated and used for making decisions in fields such as
medicine, insurance, environmental health, and transportation safety. As of this writing, the value of a statistical life is $6.9
million. Many people oppose the concept of putting a value on a human life, but the word statistical in the “value of a

statistical life” is used to ensure that we don't equate it with the true worth of a human life. Some people legitimately argue
that every life is priceless, but others argue that there are conditions in which it is impossible or impractical to save every life,
so a value must be somehow assigned to a human life in order that sound and rational decisions can be made. Not far from
the author's home, a parkway was modified at a cost of about $3 million to improve safety at a location where car occupants
had previously died in traffic crashes. In the cost benefit analysis that led to this improvement in safety, the value of a
statistical life was surely considered.
Definition
A voluntary response sample (or selfselected sample) is one in which the respondents themselves decide whether to be
included. The following types of polls are common examples of voluntary response samples. By their very nature, all are
seriously flawed because we should not make conclusions about a population on the basis of such a biased sample:
• Internet polls, in which people online can decide whether to respond
• Mailin polls, in which subjects can decide whether to reply
• Telephone callin polls, in which newspaper, radio, or television announcements ask that you voluntarily call a special
number to register your opinion
With such voluntary response samples, we can draw valid conclusions only about the specific group of people who chose to
participate; nevertheless, such samples are often incorrectly used to assert or imply conclusions about a larger population.
From a statistical viewpoint, such a sample is fundamentally flawed and should not be used for making general statements
about a larger population. The Chapter Problem involves an America OnLine poll with a voluntary response sample. See
also Examples 1 and 2, which follow.
Example 1 Voluntary Response Sample
Literary Digest magazine conducted a poll for the 1936 presidential election by sending out 10 million ballots. The magazine
received 2.3 million responses. The poll results suggested incorrectly that Alf Landon would win the presidency. In a much
smaller poll of 50,000 people, George Gallupcorrectly predicted that Franklin D. Roosevelt would win. The lesson here is
that it is not necessarily the size of the sample that makes it effective, but the sampling method. The Literary Digest ballots
were sent to magazine subscribers as well as to registered car owners and those who used telephones. On the heels of the
Great Depression, this group included disproportionately more wealthy people, who were Republicans. But the real flaw in
the Literary Digest poll is that it resulted in a voluntary response sample. In contrast, Gallup used an approach in which he
obtained a representative sample based on demographic factors. (Gallup modified his methods when he made a wrong
prediction in the famous 1948 Dewey/Truman election. Gallup stopped polling too soon, and he failed to detect a late surge
in support for Truman.) The Literary Digest poll is a classic illustration of the flaws inherent in basing conclusions on a
voluntary response sample.
Publication Bias
There is a “publication bias” in professional journals. It is the tendency to publish positive results (such as showing that some
treatment is effective) much more often than negative results (such as showing that some treatment has no effect). In the
article “Registering Clinical Trials” (Journal of the American Medical Association, Vol. 290, No. 4), authors Kay Dickersin and
Drummond Rennie state that “the result of not knowing who has performed what (clinical trial) is loss and distortion of the
evidence, waste and duplication of trials, inability of funding agencies to plan, and a chaotic system from which only certain
sponsors might benefit, and is invariably against the interest of those who offered to participate in trials and of patients in
general.” They support a process, in which all clinical trials are registered in one central system, so that future researchers
have access to all previous studies, not just the studies that were published.
Example 2 Voluntary Response Sample
The ABC television show Nightline asked viewers to call with their opinion about whether the United Nations headquarters
should remain in the United States. Viewers then decided themselves whether to call with their opinions, and 67% of
186,000 respondents said that the United Nations should be moved out of the United States. In a separate poll, 500
respondents were randomly selected and 72% of them wanted the United Nations to stay in the
United States. The two polls produced dramatically different results. Even though the Nightline poll involved 186,000
volunteer respondents, the much smaller poll of 500 randomly selected respondents is more likely to provide better results
because of the superior sampling method.
Analyze
Graph and Explore After carefully considering context, source of the data, and sampling method, we can proceed with an
analysis that should begin with appropriate graphs and explorations of the data. Graphs are discussed in Chapter 2, and
important statistics are discussed in Chapter 3.

Apply Statistical Methods Later chapters describe important statistical methods, but application of these methods is often
made easy with calculators and/or statistical software packages. A good statistical analysis does not require strong
computational skills. A good statistical analysis does require using common sense and paying careful attention to sound
statistical methods.
Conclude
Statistical Significance Statistical significance is achieved in a study when we get a result that is very unlikely to occur by
chance.
• Getting 98 girls in 100 random births is statistically significant because such an extreme event is not likely to be the result
of random chance.
• Getting 52 girls in 100 births is not statistically significant, because that event could easily occur with random chance.
Practical Significance It is possible that some treatment or finding is effective, but common sense might suggest that the
treatment or finding does not make enough of a difference to justify its use or to be practical, as illustrated in Example 3.
Example 3 Statistical Significance versus Practical Significance
In a test of the Atkins weight loss program, 40 subjects using that program had a mean weight loss of 2.1 kg (or 4.6 pounds)
after one year (based on data from “Comparison of the Atkins, Ornish, Weight Watchers, and Zone Diets for Weight Loss
and Heart Disease Risk Reduction,” by Dansinger et al., Journal of the American Medical Association, Vol. 293, No. 1).
Using formal methods of statistical analysis, we can conclude that the mean weight loss of 2.1 kg is statistically significant.
That is, based on statistical criteria, the diet appears to be effective. However, using common sense, it does not seem very
worthwhile to pursue a weight loss program resulting in such relatively insignificant results. Someone starting a weight loss
program would probably want to lose considerably more than 2.1 kg. Although the mean weight loss of 2.1 kg is statistically
significant, it does not have practical significance. The statistical analysis suggests that the weight loss program is effective,
but practical considerations suggest that the program is basically ineffective.
Detecting Phony Data
A class is given the homework assignment of recording the results when a coin is tossed 500 times. One dishonest student
decides to save time by just making up the results instead of actually flipping a coin. Because people generally cannot make
up results that are really random, we can often identify such phony data. With 500 tosses of an actual coin, it is extremely
likely that at some point, you will get a run of six heads or six tails, but people almost never include such a run when they
make up results.Another way to detect fabricated data is to establish that the results violate Benford's law: For many
collections of data, the leading digits are not uniformly distributed. Instead, the leading digits of 1, 2, …, 9 occur with rates of
30%, 18%, 12%, 10%, 8%, 7%, 6%, 5%, and 5%, respectively. (See “The Difficulty of Faking Data,” by Theodore Hill,
Chance, Vol. 12, No. 3.)
Analyzing Data: Potential Pitfalls
Here are a few more items that could cause problems when analyzing data.
Misleading Conclusions When forming a conclusion based on a statistical analysis, we should make statements that are
clear even to those who have no understanding of statistics and its terminology. We should carefully avoid making
statements not justified by the statistical analysis. For example, Section 102 introduces the concept of a correlation, or
association between two variables, such as smoking and pulse rate. A statistical analysis might justify the statement that
there is a correlation between the number of cigarettes smoked and pulse rate, but it would not justify a statement that the
number of cigarettes smoked causes a person's pulse rate to change. Such a statement about causality can be justified by
physical evidence, not by statistical analysis.
Correlation does not imply causation.
Reported Results When collecting data from people, it is better to take measurements yourself instead of asking subjects
to report results. Ask people what they weigh and you are likely to get their desired weights, not their actual weights.
Accurate weights are collected by using a scale to measure weights, not by asking people to report their weights.
Small Samples Conclusions should not be based on samples that are far too small. The Children's Defense Fund published
Children Out of School in America, in which it was reported that among secondary school students suspended in one region,
67% were suspended at least three times. But that figure is based on a sample of only three students! Media reports failed
to mention that this sample size was so small.
Loaded Questions If survey questions are not worded carefully, the results of a study can be misleading. Survey questions
can be “loaded” or intentionally worded to elicit a desired response. Here are the actual rates of “yes” responses for the two
different wordings of a question: 97% yes: “Should the President have the line item veto to eliminate waste?” 57% yes:
“Should the President have the line item veto, or not?”

Order of Questions Sometimes survey questions are unintentionally loaded by such factors as the order of the items being
considered. See the following two questions from a poll conducted in Germany, along with the very different response rates:
• “Would you say that traffic contributes more or less to air pollution than industry?” (45% blamed traffic; 27% blamed
industry.)
• “Would you say that industry contributes more or less to air pollution than traffic?” (24% blamed traffic; 57% blamed
industry.)
Nonresponse A nonresponse occurs when someone either refuses to respond to a survey question or is unavailable. When
people are asked survey questions, some firmly refuse to answer. The refusal rate has been growing in recent years, partly
because many persistent telemarketers try to sell goods or services by beginning with a sales pitch that initially sounds like it
is part of an opinion poll. (This “selling under the guise” of a poll is now called sugging.) In Lies, Damn Lies, and Statistics,
author Michael Wheeler makes this very important observation: People who refuse to talk to pollsters are likely to be
different from those who do not. Some may be fearful of strangers and others jealous of their privacy, but their refusal to talk
demonstrates that their view of the world around them is markedly different from that of those people who will let polltakers
into their homes.
Statistics Is Sexy
CareerCast.com is a job Web site, and its organizers analyzed professions using five criteria: environment, income,
employment prospects, physical demands, and stress. Based on that study, here are the top ten jobs: (1) mathematician, (2)
actuary, (3) statistician (author's emphasis), (4) biologist, (5) software engineer, (6) computer system analyst, (7) historian,
(8) sociologist, (9) industrial designer, (10) accountant. Lumberjacks are at the bottom of the list with very low pay,
dangerous work, and poor employment prospects. Reporter Steve Lohr wrote the article “For Today's Graduate, Just One
Word: Statistics” in the New York Times. In that article he quoted the chief economist at Google as saying that “the sexy job
in the next 10 years will be statisticians. And I'm not kidding.”
Missing Data Results can sometimes be dramatically affected by missing data. Sometimes sample data values are missing
because of random factors (such as subjects dropping out of a study for reasons unrelated to the study), but some data are
missing because of special factors, such as the tendency of people with low incomes to be less likely to report their incomes.
It is well known that the U.S. Census suffers from missing people, and the missing people are often from the homeless or
low income groups.
Precise Numbers Example 1 in Section 11 included a statement that there are 241,472,385 adults in the United States.
Because that figure is very precise, many people incorrectly assume that it is also accurate. In this case, that number is an
estimate, and it would be better to state that the number of adults in the United States is about 240 million.
Percentages Some studies cite misleading or unclear percentages. Keep in mind that 100% of some quantity is all of it, but
if there are references made to percentages that exceed 100%, such references are often not justified. In referring to lost
baggage, Continental Airlines ran ads claiming that this was “an area where we've already improved 100% in the last six
months.” In an editorial criticizing this statistic, the New York Times correctly interpreted the 100% improvement to mean that
no baggage is now being lost—an accomplishment that was not achieved by Continental Airlines. The following list identifies
some key principles to apply when dealing with percentages. These principles all use the basic notion that % or “percent”
really means “divided by 100.” The first principle is used often in this book.
Percentage of: To find a percentage of an amount, drop the % symbol and divide the percentage value by 100, then
multiply. This example shows that 6% of 1200 is 72: 6 % of 1200 responses = 6 100 × 1200 = 72
Fraction → Percentage: To convert from a fraction to a percentage, divide the denominator into the numerator to get an
equivalent decimal number; then multiply by 100 and affix the % symbol. This example shows that the fraction 3/4 is
equivalent to 75%: 3 4 = 0.75 → 0.75 × 100 % = 75 %
Decimal → Percentage: To convert from a decimal to a percentage, multiply by 100%. This example shows that 0.25 is
equivalent to 25%: 0.25 → 0.25 × 100 % = 25 %
Percentage → Decimal: To convert from a percentage to a decimal number, delete the % symbol and divide by 100. This
example shows that 85% is equivalent to 0.85:85 % = 85 100 = 0.85
There are many examples of the misuse of statistics. Books such as Darrell Huff's classic How to Lie with Statistics, Robert
Reichard's The Figure Finaglers, and Cynthia Crossen's Tainted Truth describe some of those other cases. Understanding
these practices will be extremely helpful in evaluating the statistical data encountered in everyday situations.

What Is Statistical Thinking? Statisticians universally agree that statistical thinking is good, but there are different views of
what actually constitutes statistical thinking. If you ask the 18,000 members of the American Statistical Association to define
statistical thinking, you will probably get 18,001 different definitions. In this section we have described statistical thinking in
terms of the ability to see the big picture; to consider such relevant factors as context, source of data, and sampling method;
and to form conclusions and identify practical implications. Statistical thinking involves critical thinking and the ability to make
sense of results. Statistical thinking might involve determining whether results are statistically significant and practically
significant. Statistical thinking demands so much more than the ability to execute complicated calculations. Through
numerous examples, exercises, and discussions, this text will help you develop the statistical thinking skills that are so
important in today's world.
13
Types of Data
Key Concept A common and important use of statistics involves collecting sample data and using them to make inferences,
or conclusions, about the population from which the data were obtained. The terms sample and population were defined in
Section 11. We should also know and understand the meanings of the terms statistic and parameter, as defined below. The
terms statistic and parameter are used to distinguish between cases in which we have data for a sample, and cases in which
we have data for an entire population. We also need to know the difference between the terms quantitative data and
categorical data. Some numbers, such as those on the shirts of basketball players, are not quantities because they don't
measure or count anything, and it would not make sense to perform calculations with such numbers. In this section we
describe different types of data. The type of data is one of the key factors that determine the statistical methods we use in
our analysis.
Parameter/Statistic
Definitions
A parameter is a numerical measurement describing some characteristic of a population.
A statistic is a numerical measurement describing some characteristic of a sample.
Hint
The alliteration in “population parameter” and “sample statistic” helps us remember the meaning of these terms.
Six Degrees of Separation
Social psychologists, historians, political scientists, and communications specialists are interested in “The Small World
Problem”: Given any two people in the world, how many intermediate links are necessary to connect the two original
people? In the 1950s and 1960s, social psychologist Stanley Milgram conducted an experiment in which subjects tried to
contact other target people by mailing an information folder to an acquaintance who they thought would be closer to the
target. Among 160 such chains that were initiated, only 44 were completed, so the failure rate was 73%. Among the
successes, the number of intermediate acquaintances varied from 2 to 10, with a median of 6 (hence “six degrees of
separation”). The experiment has been criticized for its high failure rate and its disproportionate inclusion of subjects with
above average incomes. A more recent study conducted by Microsoft researcher Eric Horvitz and Stanford Assistant
Professor Jure Leskovec involved 30 billion instant messages and 240 million people. This study found that for instant
messages that used Microsoft, the mean length of a path between two individuals is 6.6, suggesting “seven degrees of
separation.” Work continues in this important and interesting field.
Using the foregoing definitions and those given in Section 11, we see that the term statistics has two possible meanings:
1. Statistics are two or more numerical measurements describing characteristics of samples.
2. Statistics is the science of planning studies and experiments; obtaining data; organizing, summarizing, presenting,
analyzing, and interpreting those data; and then drawing conclusions based on them.
We can determine which of these two definitions applies by considering the context in which the term statistics is used. The
following example uses the first meaning of statistics as given above.
Example 1 Parameter/Statistic
In a Harris Poll, 2320 adults in the United States were surveyed about body piercings, and 5% of the respondents said that
they had a body piercing,
But not on the face. Based on the latest available data at the time of this writing, there are 241,472,385 adults in the United
States. The results from the
survey are a sample drawn from the population of all adults.
1. Parameter: The population size of 241,472,385 is a parameter, because it is based on the entire population of all adults
in the United States.

2. Statistic: The sample size of 2320 surveyed adults is a statistic, because it is based on a sample, not the entire
population of all adults in the United States. The value of 5% is another statistic, because it is also based on the sample, not
on the entire population.
Quantitative/Categorical
Some data are numbers representing counts or measurements (such as a height of 60 inches or an IQ of 135), whereas
others are attributes (such as eye color of green or brown) that are not counts or measurements. The terms quantitative
data and categorical data distinguish between these types.
Definitions
Quantitative (or numerical) data consist of numbers representing counts or measurements.
Categorical (or qualitative or attribute) data consist of names or labels that are not numbers representing counts or
measurements.
Caution
Categorical data are sometimes coded with numbers, but those numbers are actually a different way to express names.
Although such numbers might appear to be quantitative, they are actually categorical data. See the third part of Example 2.
Statistics for Online Dating
The four founders of the online dating site OkCupid are mathematicians who use methods of statistics to analyze results
from their website. The chief executive officer of OkCupid has been quoted as saying, “We're not psychologists. We're math
guys” (from “Looking for a Date? A Site Suggests You Check the Data,” by Jenna Worthman, New York Times). The
OkCupid website is unique in its use of methods of statistics to match people more effectively. By analyzing the photos and
responses of 7000 users, analysts at OkCupid found that when creating a profile photo, men should not look directly at the
camera, and they should not smile. For women, the appearance of being interesting produces much better results than the
appearance of being sexy. They found that brevity is good for the first posted message; the ideal length of the first posted
message is 40 words—about what a typical person can type in one minute.
Example 2 Quantitative/Categorical
1. Quantitative Data: The ages (in years) of survey respondents
2. Categorical Data as Labels: The political party affiliations (Democrat, Republican, Independent, other) of survey
respondents
3. Categorical Data as Numbers: The numbers 12, 74, 77, 76, 73, 78, 88, 19, 9, 23, and 25 were sewn on the jerseys of
the starting offense for the New Orleans Saints when they won a recent Super Bowl. Those numbers are substitutes for
names. They don't measure or count anything, so they are categorical data.
Include Units of Measurement With quantitative data, it is important to use the appropriate units of measurement, such as
dollars, hours, feet, or meters. We should carefully observe information given about the units of measurement, such as “all
amounts are in thousands of dollars,” “all times are in hundredths of a second,” or “all units are in kilograms.” Ignoring such
units of measurement can be very costly. NASA lost its $125 million Mars Climate Orbiter when the orbiter crashed because
the controlling software had acceleration data in English units, but they were incorrectly assumed to be in metric units.
Discrete/Continuous
Quantitative data can be further described by distinguishing between discrete and continuous types.
Definitions
Discrete data result when the data values are quantitative and the number of values is finite or “countable.” (If there are
infinitely many values, the collection of values is countable if it is possible to count them individually, such as the number of
tosses of a coin before getting tails.)
Continuous (numerical) data result from infinitely many possible quantitative values, where the collection of values is not
countable. (That is, it is impossible to count the individual items because at least some of them are on a continuous scale,
such as the lengths from 0 cm to 12 cm.)
Caution
The concept of countable data plays a key role in the preceding definitions, but it is not a particularly easy concept to
understand. Carefully study Example 3.
Example 3 Discrete/Continuous
1. Discrete Data of the Finite Type: The numbers of eggs that hens lay in one week are discrete data because they are
finite numbers, such as 5 and 7 that result from a counting process.

2. Discrete Data of the Infinite Type: Consider the number of rolls of a die required to get an outcome of 2. It is possible
that you could roll a die forever without ever getting a 2, but you can still count the number of rolls as you proceed. The
collection of rolls is countable, because you can count them, even though you might go on counting forever.
3. Continuous Data: During a year, a cow might yield an amount of milk that can be any value between 0 liters and 7000
liters. There are infinitely many values between 0 liters and 7000 liters, but it is impossible to count the number of different
possible values on such a continuous scale.
When we are describing smaller amounts, correct grammar dictates that we use “fewer” for discrete amounts and “less” for
continuous amounts. It is correct to say that we drank fewer cans of cola and that, in the process, we drank less cola. The
numbers of cans of cola are discrete data, whereas the volume amounts of cola are continuous data.
Levels of measurement
Another common way of classifying data is to use four levels of measurement: nominal, ordinal, interval, and ratio. When we
are applying statistics to real problems, the level of measurement of the data helps us decide which procedure to use. There
will be some references to these levels of measurement in this book, but the important point here is based on common
sense: Don't do computations and don't use statistical methods that are not appropriate for the data. For example, it would
not make sense to compute an average (mean) of Social Security numbers, because those numbers are data that are used
for identification, and they don't represent measurements or counts of anything.
Definition
The nominal level of measurement is characterized by data that consist of names, labels, or categories only. The data
cannot be arranged in an ordering scheme (such as low to high).
Example 4 Nominal Level
Here are examples of sample data at the nominal level of measurement.
1. Yes/No/Undecided: Survey responses of yes, no, and undecided
2. Political Party: The political party affiliations of survey respondents (Democrat, Republican, Independent, other)
3. Social Security Numbers: Social Security numbers are just substitutes for names; they do not count or measure
anything. Because nominal data lack any ordering or numerical significance, they should not be used for calculations.
Numbers such as 1, 2, 3, and 4 are sometimes assigned to the different categories (especially when data are coded for
computers), but these numbers have no real computational significance and any average (mean) calculated from them is
meaningless.
Definition
Data are at the ordinal level of measurement if they can be arranged in some order, but differences (obtained by
subtraction) between data values either cannot be determined or are meaningless.
Measuring Disobedience
How are data collected about something that doesn't seem to be measurable, such as people's level of disobedience?
Psychologist Stanley Milgram devised the following experiment: A researcher instructed a volunteer subject to operate a
control board that gave increasingly painful “electrical shocks” to a third person. Actually, no real shocks were given, and the
third person was an actor. The volunteer began with 15 volts and was instructed to increase the shocks by increments of 15
volts. The disobedience level was the point at which the subject refused to increase the voltage. Surprisingly, twothirds of the
subjects obeyed orders even when the actor screamed and faked a heart attack.
Example 5 Ordinal Level
Here are examples of sample data at the ordinal level of measurement.
1. Course Grades: A college professor assigns grades of A, B, C, D, or F. These grades can be arranged in order, but we
can't determine differences between the grades. For example, we know that A is higher than B (so there is an ordering), but
we cannot subtract B from A (so the difference cannot be found).
2. Ranks: U.S. News & World Report ranks colleges. As of this writing, Harvard was ranked first and Princeton was ranked
second. Those ranks of 1 and 2 determine an ordering, but the difference between those ranks is meaningless. The
difference of “second minus first” might suggest 2 − 1 = 1 but this difference of 1 is meaningless because it is not an exact
quantity that can be compared to other such differences. The difference between Harvard and Princeton cannot be
quantitatively compared to the difference between Yale and Columbia, the universities ranked third and fourth, respectively.
Ordinal data provide information about relative comparisons, but not the magnitudes of the differences. Usually, ordinal data
should not be used for calculations such as an average, but this guideline is sometimes ignored (such as when we use letter
grades to calculate a grade point average).

Definition
Data are at the interval level of measurement if they can be arranged in order, and differences between data values can
be found and are meaningful. Data at this level do not have a natural zero starting point at which none of the quantity is
present.
Example 6 Interval Level
These examples illustrate the interval level of measurement.
1. Temperatures: Outdoor temperatures of 40 ° F and 90 ° F are examples of data at this interval level of measurement.
Those values are ordered, and we can determine their difference of 50 ° F . However, there is no natural starting point. The
value of 0 ° F might seem like a starting point, but it is arbitrary and does not represent the total absence of heat.
2. Years: The years 1492 and 1776 can be arranged in order, and the difference of 284 years can be found and is
meaningful. However, time did not begin in the year 0, so the year 0 is arbitrary instead of being a natural zero starting point
representing “no time.”
Definition
Data are at the ratio level of measurement if they can be arranged in order, differences can be found and are meaningful,
and there is a natural zero starting point (where zero indicates that none of the quantity is present). For data at this level,
differences and ratios are both meaningful.
Example 7 Ratio Level
The following are examples of data at the ratio level of measurement. Note the presence of the natural zero value, and also
note the use of meaningful ratios of “twice” and “three times.”
1. Car Lengths: Car lengths of 106 in. for a Smart car and 212 in. for a Mercury Grand Marquis (0 in. represents no length,
and 212 in. is twice as long as 106 in.)
2. Class Times: The times of 50 min and 100 min for a statistics class (0 min represents no class time, and 100 min is twice
as long as 50 min.)
Hint
This level of measurement is called the ratio level because the zero starting point makes ratios meaningful, so here is an
easy test to determine whether values are at the ratio level: Consider two quantities where one number is twice the other,
and ask whether “twice” can be used to correctly describe the quantities. Because a person with a height of 6 ft is twice as
tall as a person with a height of 3 ft, the heights are at the ratio level of measurement. In contrast, 50 ° F is not twice as hot
as 25 ° F , so Fahrenheit temperatures are not at the ratio level. See Table 12.

14
Collecting Sample Data
Key Concept An absolutely critical concept in applying methods of statistics is consideration of the method used to collect
the sample data. Of particular importance is the method of using a simple random sample. We will make frequent use of this
sampling method throughout the remainder of this book.
As you read this section, remember this:
If sample data are not collected in an appropriate way, the data may be so utterly useless that no amount of
statistical torturing can salvage them.
Part 1 of this section introduces the basics of data collection, and Part 2 describes some common ways in which
observational studies and experiments are conducted.
Part 1: Basics of Collecting Data
Statistical methods are driven by the data that we collect. We typically obtain data from two distinct sources: observational
studies and experiments.

Definitions
In an observational study, we observe and measure specific characteristics, but we don't attempt to modify the subjects
being studied. In an experiment, we apply some treatment and then proceed to observe its effects on the subjects.
(Subjects in experiments are called experimental units.) Experiments are often better than observational studies, because
experiments typically reduce the chance of having the results affected by some variable that is not part of a study. (A lurking
variable is one that affects the variables included in the study, but it is not included in the study.) In one classic example, we
could use an observational study to incorrectly conclude that ice cream causes drownings based on data showing that
increases in ice cream sales are associated with increases in drownings. Our error is to miss the lurking variable of
temperature and thus fail to recognize that warmer months result in both increased ice cream sales and increased
drownings. If, instead of using data from an observational study, we conducted an experiment with one group treated with
ice cream while another group got no ice cream, we would see that ice cream consumption has no effect on drownings.
Example 1 Observational Study and Experiment
Observational Study: The typical survey is a good example of an observational study. For example, the Pew Research
Center surveyed 2252 adults in the United States and found that 59% of them go online wirelessly. The respondents were
asked questions, but they were not given any treatment, so this is an example of an observational study.
Experiment: In the largest public health experiment ever conducted, 200,745 children were given a treatment consisting of
the Salk vaccine, while 201,229 other children were given a placebo. The Salk vaccine injections constitute a treatment that
modified the subjects, so this is an example of an experiment. Clinical Trials vs. Observational Studies
In a New York Times article about hormone therapy for women, reporter Denise Grady wrote about randomized clinical trials
that involve subjects who were randomly assigned to a treatment group and another group not given the treatment. Such
randomized clinical trials are often referred to as the “gold standard” for medical research. In contrast, observational studies
can involve patients who decide themselves to undergo some treatment. Subjects who decide themselves to undergo
treatments are often healthier than other subjects, so the treatment group might appear to be more successful simply
because it involves healthier subjects, not necessarily because the treatment is effective. Researchers criticized
observational studies of hormone therapy for women by saying that results might appear to make the treatment more
effective than it really is. Whether one is conducting an observational study or an experiment, it is important to select the
sample of subjects in such a way that the sample is likely to be representative of the larger population. In Section 12 we saw
that in a voluntary response sample, the subjects decide themselves whether to respond. Although voluntary response
samples are very common, their results are generally useless for making valid inferences about larger populations. The
following definition refers to one common and effective way to collect sample data.
Definition
A simple random sample of n subjects is selected in such a way that every possible sample of the same size n has the
same chance of being chosen. (A simple random sample is often called a random sample, but strictly speaking, a random
sample has the weaker requirement that all members of the population have the same chance of being selected. That
distinction is not so important in this text.)
Throughout, we will use various statistical procedures, and we often have a requirement that we have collected a
simple random sample, as defined above.
The definition of a simple random sample requires more than selecting subjects in such a way that each has the same
chance of being selected. Consider the selection of three students from the class of six students depicted below. If you use a
coin toss to select a row, randomness is used and each student has the same chance of being selected, but the result is not
a simple random sample. The coin toss will produce only two possible samples; some samples of three students have no
chance of being selected, such as a sample consisting of a female and two males. This violates the requirement that all
samples of the same size have the same chance of being selected. Instead of the coin toss, you could get a simple random
sample of three students by writing each of the six different student names on separate index cards, which could then be
placed in a bowl and mixed. The selection of three index cards will yield a simple random sample, because every different
possible sample of three students now has the same chance of being selected.

With random sampling we expect all components of the population to be (approximately) proportionately represented.
Random samples are selected by many different methods, including the use of computers to generate random numbers.
Unlike careless or haphazard sampling, random sampling usually requires very careful planning and execution. Wayne
Barber of Chemeketa Community College is quite correct when he tells his students that “randomness needs help.”
Other Sampling Methods In addition to simple random sampling, here are some other sampling methods commonly used
for surveys. Figure 13
illustrates these different sampling methods.

Definitions
In systematic sampling, we select some starting point and then select every kth (such as every 50th) element in the
population.
With convenience sampling, we simply use results that are very easy to get.
In stratified sampling, we subdivide the population into at least two different subgroups (or strata) so that subjects within
the same subgroup share the same characteristics (such as age bracket). Then we draw a sample from each subgroup (or
stratum).
In cluster sampling, we first divide the population area into sections (or clusters). Then we randomly select some of those
clusters and choose all the members from those selected clusters.

Hawthorne and Experimenter Effects
The wellknown placebo effect occurs when an untreated subject incorrectly believes that he or she is receiving a real
treatment and reports an improvement in symptoms. The Hawthorne effect occurs when treated subjects somehow respond
differently, simply because they are part of an experiment. (This phenomenon was called the “Hawthorne effect” because it
was first observed in a study of factory workers at Western Electric's Hawthorne plant.) An experimenter effect (sometimes
called a Rosenthal effect) occurs when the researcher or experimenter unintentionally influences subjects through such
factors as facial expression, tone of voice, or attitude.
It is easy to confuse stratified sampling and cluster sampling, because they both use subgroups. But cluster sampling uses
all members from a sample of clusters, whereas stratified sampling uses a sample of members from all strata. An example
of cluster sampling is a preelection poll, in which pollsters randomly select 30 election precincts from a large number of
precincts and then survey all voters in each of those precincts. This is faster and much less expensive than selecting one
voter from each of the many precincts in the population area. Pollsters can adjust or weight the results of stratified or cluster
sampling to correct for any disproportionate representation of groups.
For a fixed sample size, if you randomly select subjects from different strata, you are likely to get more consistent (and less
variable) results than by simply selecting a random sample from the general population. For that reason, pollsters often use
stratified sampling to reduce the variation in the results. Many of the methods discussed later in this book require that
sample data be derived from a simple random sample, and neither stratified sampling nor cluster sampling satisfies that
requirement.
Multistage Sampling Professional pollsters and government researchers often collect data by using some combination of
the basic sampling methods. In a multistage sample design, pollsters select a sample in different stages, and each stage
might use different methods of sampling. For example, one multistage sample design might involve the random selection of
clusters, but instead of surveying all members of the chosen clusters, you might randomly select 50 men and 50 women in
each selected cluster; thus you begin with cluster sampling and end with stratified sampling. See Example 2 for an actual
multistage sample design that is complex, but effective.
Example 2 Multistage Sample Design
The U.S. government's unemployment statistics are based on surveyed households. It is impractical to personally visit each
member of a simple random sample, because individual households are spread all over the country. Instead, the U.S.
Census Bureau and the Bureau of Labor Statistics collaborate to conduct a survey called the Current Population Survey.
This survey obtains data describing such factors as unemployment rates, college enrollments, and weekly earnings
amounts. One recent survey incorporates a multistage sample design, roughly following these steps:
1. The entire United States is partitioned into 2025 different regions called primary sampling units (PSUs). The primary
sampling units are metropolitan areas, large counties, or combinations of smaller counties. These primary sampling units are
geographically connected. The 2025 primary sampling units are then grouped into 824 different strata.
2. In each of the 824 different strata, one of the primary sampling units is selected so that the probability of selection is
proportional to the size of the population in each primary sampling unit.
3. In each of the 824 selected primary sampling units, census data are used to identify a census enumeration district, with
each containing about 300 households. Enumeration districts are then randomly selected.
4. In each of the selected enumeration districts, clusters of about four addresses (contiguous whenever possible) are
randomly selected.
5. Respondents in the 60,000 selected households are interviewed about the employment status of each household member
of age 16 or older.
This multistage sample design includes random, stratified, and cluster sampling at different stages. The end result is a very
complicated sampling design, but it is much more practical and less expensive than using a simpler design, such as a
simple random sample.
Part 2: Beyond the Basics of Collecting Data
In Part 2 of this section, we refine what we've learned about observational studies and experiments by discussing different
types of observational studies and different ways of designing experiments.
There are various types of observational studies in which investigators observe and measure characteristics of subjects. The
following definitions identify the standard terminology used in professional journals for different types of observational
studies. These definitions are illustrated in Figure 14.

Definitions
In a crosssectional study, data are observed, measured, and collected at one point in time, not over a period of time.
In a retrospective (or casecontrol) study, data are collected from a past time period by going back in time (through
examination of records, interviews, and so on).
In a prospective (or longitudinal or cohort) study, data are collected in the future from groups that share common factors
(such groups are called cohorts).
The sampling done in retrospective studies differs from that in prospective studies. In retrospective studies we go back in
time to collect data about the characteristic that is of interest, such as a group of drivers who died in car crashes and another
group of drivers who did not die in car crashes. In prospective studies we go forward in time by following a group with a
potentially causative factor and a group without it, such as a group of drivers who use cell phones and a group of drivers
who do not use cell phones.
Designs of Experiments
We begin with Example 3, which describes the largest public health experiment ever conducted, and which serves as an
example of an experiment having a good design. After describing the experiment in more detail, we describe the
characteristics of randomization, replication, and blinding that typify a good design in experiments.
Example 3 the Salk Vaccine Experiment
In 1954, a large-scale experiment was designed to test the effectiveness of the Salk vaccine in preventing polio, which had
killed or paralyzed thousands of children. In that experiment, 200,745 children were given a treatment consisting of Salk
vaccine injections, while a second group of 201,229 children were injected with a placebo that contained no drug. The
children being injected did not know whether they were getting the Salk vaccine or the placebo. Children were assigned to
the treatment or placebo group through a process of random selection, equivalent to flipping a coin.
Among the children given the Salk vaccine, 33 later developed paralytic polio, and among the children given a placebo, 115
later developed paralytic polio.
Randomization is used when subjects are assigned to different groups through a process of random selection. The 401,974
children in the Salk vaccine experiment were assigned to the Salk vaccine treatment group or the placebo group via a
process of random selection equivalent to flipping a coin. In this experiment, it would be extremely difficult to directly assign
children to two groups having similar characteristics of age, health, sex, weight, height, diet, and so on. There could easily
be important variables that we might not think of including. The logic behind randomization is to use chance as a way to
create two groups that are similar. Although it might seem that we should not leave anything to chance in experiments,
randomization has been found to be an extremely effective method for assigning subjects to groups. However, it is possible
for randomization to result in unbalanced samples, especially when very small sample sizes are involved.
Replication is the repetition of an experiment on more than one subject. Samples should be large enough so that the erratic
behavior that is characteristic of very small samples will not disguise the true effects of different treatments. Replication is
used effectively when we have enough subjects to recognize differences resulting from different treatments. (In another
context, replication refers to the repetition or duplication of an experiment so that results can be confirmed or verified.) With
replication, the large sample sizes increase the chance of recognizing different treatment effects. However, a large sample is
not necessarily a good sample. Although it is important to have a sample that is sufficiently large, it is even more important to
have a sample in which subjects have been chosen in some appropriate way, such as random selection.

Use a sample size that is large enough to let us see the true nature of any effects, and obtain the sample using an
appropriate method, such as one based on randomness.
In the experiment designed to test the Salk vaccine, 200,745 children were given the actual Salk vaccine and 201,229 other
children were given a placebo. Because the actual experiment used sufficiently large sample sizes, the researchers could
observe the effectiveness of the vaccine.
Blinding is in effect when the subject doesn't know whether he or she is receiving a treatment or a placebo. Blinding
enables us to determine whether the treatment effect is significantly different from a placebo effect, which occurs when an
untreated subject reports an improvement in symptoms. (The reported improvement in the placebo group may be real or
imagined.) Blinding minimizes the placebo effect or allows investigators to account for it. The polio experiment was
doubleblind, which means that blinding occurred at two levels: (1) The children being injected didn't know whether they
were getting the Salk vaccine or a placebo, and (2) the doctors who gave the injections and evaluated the results did not
know either. Codes were used so that the researchers could objectively evaluate the effectiveness of the Salk vaccine.
Controlling Effects of Variables Results of experiments are sometimes ruined because of confounding.
Definition
Confounding occurs in an experiment when the investigators are not able to distinguish among the effects of different
factors.
Try to design the experiment in such a way that confounding does not occur.
Designs of Experiments See Figure 15( a), where confounding can occur when the treatment group of women shows
strong positive results. Here the treatment group consists of women and the placebo group consists of men. Confounding
has occurred because we cannot determine whether the treatment or the gender of the subjects caused the positive results.
It is important to design experiments in such a way as to control and understand the effects of the variables (such as
treatments). The Salk vaccine experiment in Example 3 illustrates one method for controlling the effect of the treatment
variable: Use a completely randomized experimental design, whereby randomness is used to assign subjects to the
treatment group and the placebo group. A completely randomized experimental design is one of the following methods that
are used to control effects of variables.
Completely Randomized Experimental Design: Assign subjects to different treatment groups through a process of
random selection, as illustrated in Example 3 and Figure 15(b).
Randomized Block Design: A block is a group of subjects that are similar, but blocks differ in ways that might affect the
outcome of the experiment. Use the following procedure, as illustrated in Figure 15( c):
1. Form blocks (or groups) of subjects with similar characteristics.
2. Randomly assign treatments to the subjects within each block.

For example, in designing an experiment to test the effectiveness of aspirin treatments on heart disease, we might form a
block of men and a block of women, because it is known that the hearts of men and women can behave differently. By
controlling for gender, this randomized block design eliminates gender as a possible source of confounding.
A randomized block design uses the same basic idea as stratified sampling, but randomized block designs are used when
designing experiments, whereas stratified sampling is used for surveys.
Matched Pairs Design: Compare two treatment groups (such as treatment and placebo) by using subjects matched in pairs
that are somehow related or have similar characteristics, as in the following cases.
• Before/After: Matched pairs might consist of measurements from subjects before and after some treatment, as illustrated in
Figure 15(
d). Each subject yields a “before” measurement and an “after” measurement, and each before/after pair of measurements is
a matched pair.
• Twins: A test of Crest toothpaste used matched pairs of twins, where one twin used Crest and the other used another
toothpaste.
Rigorously Controlled Design: Carefully assign subjects to different treatment groups, so that those given each treatment
are similar in the ways that are important to the experiment. In an experiment testing the effectiveness of aspirin on heart
disease, if the placebo group includes a 27yearold male smoker who drinks heavily and consumes an abundance of salt and
fat, the treatment group should also include a person with these characteristics (such a person would be easy to find). This
approach can be extremely difficult to implement, and often we can never be sure that we have accounted for all of the
relevant factors
Sampling Errors In an algebra course, you will get the correct result if you use the correct methods and apply them
correctly. In statistics, you could use a good sampling method and do everything correctly, and yet it is possible for the result
to be wrong. No matter how well you plan and execute the sample collection process, there is likely to be some error in the
results. Suppose that you randomly select 1000 adults, ask them whether they use a cell phone while driving, and record the
sample percentage of “yes” responses. If you randomly select another sample of 1000 adults, it is likely that you will obtain a
different sample percentage. The different types of sampling errors are described here.
Definitions
A sampling error (or random sampling error) occurs when the sample has been selected with a random method, but there
is a discrepancy between a sample result and the true population result; such an error results from chance sample
fluctuations.
A nonsampling error is the result of human error, including such factors as wrong data entries, computing errors, questions
with biased wording, false data provided by respondents, forming biased conclusions, or applying statistical methods that
are not appropriate for the circumstances.
A nonrandom sampling error is the result of using a sampling method that is not random, such as using a convenience
sample or a voluntary response sample.
If we carefully collect a random sample so that it is representative of the population, we can use methods in this book to
analyze the sampling error, but we must exercise great care to minimize nonsampling error. Experimental design requires
much more thought and care than we can describe in this relatively brief section. Taking a complete course in the design of
experiments is a good start in learning so much more about this important topic.