Chapter 01

Statistics
Chapter 1: Statistics, Data and Statistical Thinking

McClave, Statistics, 11th ed. Chapter 1:
Statistics, Data and Statistical Thinking
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Where We’re Going
 Introduction to the field of statistics
 How statistics applies to real-world problems
 Establish the link between statistics and
data
 Differentiate between population and
sample data
 Differentiate between descriptive and
inferential statistics

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1.1: The Science of Statistics
 Statistics is the science of data. This involves
collecting, classifying, summarizing, organizing,
analyzing and interpreting numerical
information.

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1.2: Types of Statistical
Applications

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Applications
 Descriptive statistics utilizes numerical
and graphical methods to look for patterns
in a data set, to summarize the information
revealed in a data set and to present that
information in a convenient form.

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Applications
 Inferential statistics utilizes sample data
to make estimates, decisions, predictions
or other generalizations about a larger set
of data.

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1.3: Fundamental Elements of
Statistics
 An experimental unit is an object
about which we collect data.
 Person
 Place
 Thing
 Event

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Statistics
 An population is a set of units in
which we are interested.
 Typically, there are too many
experimental units in a population to
consider every one.
 If we can examine every single one, we
conduct a census.

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Statistics
 A variable is a characteristic or
property of an individual unit.
 The values of these characteristics will,
not surprisingly, vary.

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Statistics
 A sample is a
subset of the
population.

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Statistics
 A measure of reliability is a
statement about the degree of
uncertainty associated with a statistical
inference.
Based on our analysis, we think 56% of soda
drinkers prefer Pepsi to Coke, ± 5%.

Statistics
Descriptive Statistics
 The population or
sample of interest
 One or more variables to
be investigated
 Tables, graphs or
numerical summary tools
 Identification of patterns
in the data
Inferential Statistics
 Population of interest
 One or more variables to
be investigated
 The sample of
population units
 The inference about the
population based on the
sample data
 A measure of reliability
of the inference
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1.4: Types of Data
 Quantitative Data are measurements
that are recorded on a naturally
occurring numerical scale.
 Age
 GPA
 Salary
 Cost of books this semester
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1.4: Types of Data
 Qualitative Data are measurements
that cannot be recorded on a natural
numerical scale, but are recorded in
categories.
 Year in school
 Live on/off campus
 Major
 Gender
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1.5: Collecting Data
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 Published
Source
 Designed
Experiment
 Survey
 Observational
Study
SOURCE: United States Department of Agriculture
Foreign Agricultural Service

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 Published Source
 Journal
 Book
 Newspaper
 Magazine
 (Reliable) Web Site

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 Designed Experiment
 Strict control over the experiment and the
units in the experiment

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 Survey
 Gallup, Harris and other polls
 Nielsen

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 Observational Study
 Observe units in natural settings
 No control over behavior of units

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 A representative sample exhibits
characteristics typical of those
possessed by the target population.
 A random sample of n units is
selected in such a way that every
different sample of size n has the
same chance of being selected.

1.6: The Role of Statistics in
Critical Thinking
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 Statistical thinking involves applying
rational thought and the science of
statistics to critically assess data and
inferences.

Critical Thinking
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 Selection bias results when a subset
of the experimental units in the
population have been excluded so that
these units have no chance of being
selected in the sample.
LANDON IN A LANDSLIDE
In 1936 The Literary Digest predicts Governor Alf Landon of
Kansas would defeat President Roosevelt with 57% of the
popular vote and 370 electoral votes, the result of polling
primarily affluent voters.

Critical Thinking
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 Nonresponse bias results when the
researchers conducting a survey or
study are unable to obtain data on all
experimental units selected for the
sample.

Critical Thinking
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 Measurement error refers to
inaccuracies in the values of the data
recorded. In surveys, this kind of error
may be due to ambiguous or leading
questions and the interviewer’s effect
on the respondent.
“Do you prefer Candidate X, father of three and church elder,
or Candidate Y, who got the nomination despite his shady past?”

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