Statistical Modeling for Communication Research
Chapter 1
Concept Meaning
inferential statistics Techniques for making statements about
a larger set of observations from data
collected for a smaller set of
observations.
generalization We want to generalize a statement about
the sample to a statement about the
population from which the sample was
drawn.
population The large set of observations about
which we want to make a statement.
random sample Sampling technique in which each sample
has an equal probability of being chosen.
sample statistic A number describing a characteristic of a
sample.
sampling space All possible sample statistic values
random variable A variable with values that depend on
chance.
sampling distribution All possible sample statistic values and
their probabilities or probability densities
probability Proportion of all possible samples that
we could have drawn that happen to
contain x yellow candies.
probability distribution A function that is used to give the
probability of all the possible values that
a random variable can take.
discrete probability distribution Only a limited number of outcomes is
possible; it is feasible to list the
probability of each outcome separately.
expected value/expectation The mean of a probability distribution,
such as a sampling distribution.
unbiased estimator A sample statistic for which the expected
value equals the population value.
parameter The population statistic.
(downward) biased Say: you find 2 yellow candies within a
sample of 10 candies.
If you conclude that the population
contains 2 yellow candies, you vastly
underestimate the actual value.
, If you use proportions (0.2 out of 1 in
the population is yellow) you’d be more
right.
representative sample A sample is representative of a population
(in the strict sense) if variables in the
sample are distributed in the same way as
in the population.
continuous variable We can always think of a new value in
between two values. ( 1 and 2 would have
an infinite number of values in between:
1.001, 1.02, 1.023).
continuous probability distribution A probability distribution in which the
random variable X can take on any value
(is continuous). Because there are infinite
values that X could assume, the
probability of X taking on any one
specific value is zero.
probability density A means of getting the probability that a
continuous random variable (like a sample
statistic) falls within a particular range.
(left-hand/right-hand) probability probability of values up to (and
including) a threshold value, which is
known as a left-hand probability, or the
probability of values above (and
including) a threshold value, which is
called a right-hand probability.
Chapter 2
Concept Meaning
bootstrapping/bootstrap sample Sampling with replacement from the
original sample to create a sampling
distribution
So: You draw one original sample, and
you draw 5000 more samples from that
sample to create a distribution. That is
bootstrapping.
sampling with replacement Obtain different bootstrap samples
from the original samples; it is possible
to choose a thing that’s been sampled
more than once in order to get different
samples for bootstrapping.
exact approach Calculating the true sampling
distribution as the probabilities of
Chapter 1
Concept Meaning
inferential statistics Techniques for making statements about
a larger set of observations from data
collected for a smaller set of
observations.
generalization We want to generalize a statement about
the sample to a statement about the
population from which the sample was
drawn.
population The large set of observations about
which we want to make a statement.
random sample Sampling technique in which each sample
has an equal probability of being chosen.
sample statistic A number describing a characteristic of a
sample.
sampling space All possible sample statistic values
random variable A variable with values that depend on
chance.
sampling distribution All possible sample statistic values and
their probabilities or probability densities
probability Proportion of all possible samples that
we could have drawn that happen to
contain x yellow candies.
probability distribution A function that is used to give the
probability of all the possible values that
a random variable can take.
discrete probability distribution Only a limited number of outcomes is
possible; it is feasible to list the
probability of each outcome separately.
expected value/expectation The mean of a probability distribution,
such as a sampling distribution.
unbiased estimator A sample statistic for which the expected
value equals the population value.
parameter The population statistic.
(downward) biased Say: you find 2 yellow candies within a
sample of 10 candies.
If you conclude that the population
contains 2 yellow candies, you vastly
underestimate the actual value.
, If you use proportions (0.2 out of 1 in
the population is yellow) you’d be more
right.
representative sample A sample is representative of a population
(in the strict sense) if variables in the
sample are distributed in the same way as
in the population.
continuous variable We can always think of a new value in
between two values. ( 1 and 2 would have
an infinite number of values in between:
1.001, 1.02, 1.023).
continuous probability distribution A probability distribution in which the
random variable X can take on any value
(is continuous). Because there are infinite
values that X could assume, the
probability of X taking on any one
specific value is zero.
probability density A means of getting the probability that a
continuous random variable (like a sample
statistic) falls within a particular range.
(left-hand/right-hand) probability probability of values up to (and
including) a threshold value, which is
known as a left-hand probability, or the
probability of values above (and
including) a threshold value, which is
called a right-hand probability.
Chapter 2
Concept Meaning
bootstrapping/bootstrap sample Sampling with replacement from the
original sample to create a sampling
distribution
So: You draw one original sample, and
you draw 5000 more samples from that
sample to create a distribution. That is
bootstrapping.
sampling with replacement Obtain different bootstrap samples
from the original samples; it is possible
to choose a thing that’s been sampled
more than once in order to get different
samples for bootstrapping.
exact approach Calculating the true sampling
distribution as the probabilities of
