Datasheet

11
Chapter 1: Evaluating Data in the Real World
Estimating the population average is one kind of inference that statisticians
make from sample data. I discuss inference in more detail in the upcoming
section “Inferential Statistics.”
Now for some terminology you have to know: Characteristics of a population
(like the population average) are called parameters, and characteristics of a
sample (like the sample average) are called statistics. When you confine your
field of view to samples, your statistics are descriptive. When you broaden
your horizons and concern yourself with populations, your statistics are
inferential.
Now for a notation convention you have to know: Statisticians use Greek let-
ters (μ, σ, ρ) to stand for parameters, and English letters
, s, r) to stand for
statistics. Figure 1-1 summarizes the relationship between populations and
samples, and parameters and statistics.
Figure 1-1:
The rela-
tionship
between
populations,
samples,
parameters,
and
statistics.
Statistics
Parameters
Select
individuals
Make
inferences
about
Population
Sample
Variables: Dependent and independent
Simply put, a variable is something that can take on more than one value.
(Something that can have only one value is called a constant.) Some variables
you might be familiar with are today’s temperature, the Dow Jones Industrial
Average, your age, and the value of the dollar against the euro.
Statisticians care about two kinds of variables, independent and dependent.
Each kind of variable crops up in any study or experiment, and statisticians
assess the relationship between them.
For example, imagine a new way of teaching reading that’s intended to
increase the reading speed of fifth-graders. Before putting this new method
into schools, it would be a good idea to test it. To do that, a researcher would
randomly assign a sample of fifth-grade students to one of two groups: One
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