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SMCR Summary

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  • Course
  • Statistical Modelling for Communication Research
  • Institution
  • Universiteit Van Amsterdam (UvA)

A full summary of everything you need to know to pass the Statistical Modeling for Communication Research Exam (from the Communication Science Bachelor degree at the University of Amsterdam). This summary is a simpler version of the web-book "A Gentle but Critical Introduction to Statistical Infe...

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Document information

  • October 29, 2019
  • 82
  • 2019/2020
  • Summary

Subjects

  • smcr
  • university of amsterdam
  • communication science
  • statistics
  • uva
  • statistical modeling for communication research

Written for

  • Universiteit van Amsterdam (UvA)
  • Communicatiewetenschap
  • Statistical Modelling for Communication Research
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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​.

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