Statistical Modelling for Communication Research Exam
Chapter 1: Sampling Distribution
1.1 Statistical inferences
A researcher wants to make general statements (=statements that apply to many situations)
about the observable world.
Checking these statements requires collecting lots of data from that world. However, collecting
data is expensive. Therefore, a researcher tries to collect as little data as necessary.
Inferential statistics offers techniques for making statements about a larger set of observations
from data collected for a smaller set of observations.
● The large set of observations about which we want to make a statement is called the
population
● The smaller set is called a sample
We want to g eneralize a statement about the sample to a statement about the population from
which the sample was drawn. Traditionally, statistical inference is generalization from the data
collected in a r andom sample.
1.2 A discrete random variable (=a variable which can only take a countable number of values)
Sample statistics is a number describing a characteristic of the sample. For instance, one bag
contains 4 yellow candies, another bag contains 7, and so on.
The sample statistic is called a r andom variable. For instance, ‘the amount of yellow candies in
a bag’ i s a variable. It is a variable because it assigns an outcome score to a sample and
different samples can have different scores.
All possible o
utcome scores constitute the sampling space. For instance, a bag of ten candies
may contain 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 yellow candies. The numbers 0 to 10 are the
sampling space of the sample statistic number of yellow candies in a bag.
Some sample statistic outcomes occur more often than other outcomes. We can see this if we
draw very many random samples from a population and collect the frequencies of all outcome
scores in a table or chart. The distribution of the outcome scores of very many samples is a
sampling distribution.
Chapter 1: Sampling Distribution
1.1 Statistical inferences
A researcher wants to make general statements (=statements that apply to many situations)
about the observable world.
Checking these statements requires collecting lots of data from that world. However, collecting
data is expensive. Therefore, a researcher tries to collect as little data as necessary.
Inferential statistics offers techniques for making statements about a larger set of observations
from data collected for a smaller set of observations.
● The large set of observations about which we want to make a statement is called the
population
● The smaller set is called a sample
We want to g eneralize a statement about the sample to a statement about the population from
which the sample was drawn. Traditionally, statistical inference is generalization from the data
collected in a r andom sample.
1.2 A discrete random variable (=a variable which can only take a countable number of values)
Sample statistics is a number describing a characteristic of the sample. For instance, one bag
contains 4 yellow candies, another bag contains 7, and so on.
The sample statistic is called a r andom variable. For instance, ‘the amount of yellow candies in
a bag’ i s a variable. It is a variable because it assigns an outcome score to a sample and
different samples can have different scores.
All possible o
utcome scores constitute the sampling space. For instance, a bag of ten candies
may contain 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 yellow candies. The numbers 0 to 10 are the
sampling space of the sample statistic number of yellow candies in a bag.
Some sample statistic outcomes occur more often than other outcomes. We can see this if we
draw very many random samples from a population and collect the frequencies of all outcome
scores in a table or chart. The distribution of the outcome scores of very many samples is a
sampling distribution.
