Class 1.
Introduction
Prof. Pieter-Paul Verhaeghe
Ta. Mattias Van Hulle
Ta. Dounia Bourabain

Why study statistics?
“There are lies, damned lies
and statistics.”
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AIM? • To read most policy reports
• To evaluate measures or
policies
• To properly read news papers
• To grasp our society
• To analyse media behaviour
• To make a business plan
• To win sport competitions
• To discern lies from statistics
• …
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• Every Wednesday from 10am to 12am
• Room: D.0.03
• Lecturer
– Prof. Pieter-Paul Verhaeghe
– Pieter-paul.verhaeghe@vub.be
• Attendance is mandatory
• Print or download the slides beforehand
Practicalities
Lectures
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• Seminars: to practice the statistics in small groups
• Room: B.0.02, B.0.04, B.0.05 or B.0.06 (could be
changed)
• Six to seven groups:
– Groups 1 & 2: Wednesday from 3pm to 6pm
– Group 3: Thursday from 9pm to 12pm
– Groups 4 & 5: Thursday from 1pm to 4pm
– Groups 6 & 7: Friday from 9am to 12am
• Check PointCarré to find out in which group you are and
the exact location
Practicalities
Statistical seminars
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Mattias Van Hulle
TA for Groups 1 & 4 & 6
Mattias.van.hulle@vub.be
Practicalities
Teaching assistants
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Dounia Bourabain
TA for Groups 2 & 3 & 5 & 7
Dounia.bourabain@vub.be

1. Slides of all lectures and statistical seminars
• can be downloaded on PointCarré
2. Handbook ‘Statistical methods for the social
sciences’ of Agresti & Finlay (2014, 4th
international edition)
• can be bought through the bookshop of the VUB =
VUBTiek - https://my.vub.ac.be/en/bookshop
Practicalities
Study material
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Practicalities
PointCarré
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• Two tests: 20% of the points
– Test 1: during seminar 4 (25, 26 or 27th Oct)
– Test 2: during seminar 8 (29, 30th Nov or 1st Dec)
• Exam: 60% of the points
• Assignments: 20% of the points
How to pass this course?
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• Statistics = 6 ECTS study points = 178 hours study
time
– Classes and lectures: 65 hours
– Study at home: 113 hours (>14 days of 8 hours)
Practicalities
How to pass this course?
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What is statistics?
• Statistics = body of methods for obtaining and
analysing data
– Gathering the data
– Summarizing the data
– Interpreting the data
Introduction to SPC
methodology
This course
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What is statistics?
• Population: total set of subjects of interests in a
research
– Subjects = statistical units
– E.g. people, families, schools, cities, countries…
– Requires a clear definition that circumscribes the
population: who’s in and who’s out?
– Population  population data
– Number of subjects = population size
– Statistical notation for population size = N
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What is statistics?
• Sample: a smaller subset of subjects selected
from the research population
– simple random selection  representative sample
– Sample  sample data
– Number of sampled subjects = sample size
– Statistical notation for the sample size: n
– Sample size ≤ population size
– n ≤ N
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What is statistics?
• Descriptive statistics summarise the
information from the data.
• Inferential statistics provide predictions about
a population, based on data from a sample of
that population.
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Variables
• Subjects in a population or sample vary from
each other with respect to a characteristic
• Data about this variability
• Variable = a characteristic that can vary in value
among subjects/statistical units in a sample or
population
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Variables
• Statistical notation for variables: X, Y, Z…
• Each subject has a particular value on a variable:
Y1, Y2, … , Yn
• Example of a sample of 8 people
– Sample size n = 8
– Variable Y = gender
– Values of the subjects on variable Y:
Y1 = man; Y2 = man; Y3 = woman; Y4 = man; Y5 =
woman; Y6 = woman; Y7 = man; Y8 = woman
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Variables
• Number of different values a variable can take:
m
• Measurement scale
– All values the variable can take = Y1, Y2, … , Ym
– Number of different values ≤ sample size
– m ≤ n
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Variables
• Example of a sample of 8 people
– Sample size n = 8
– Variable Y = gender
– Values of the subjects on variable Y: Y1 = man; Y2 =
man; Y3 = woman; Y4 = man; Y5 = woman; Y6 =
woman; Y7 = man; Y8 = woman
– Number of different values m = 2
– Measurement scale = man, woman
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Variables
• Univariate statistics: 1 variable
• Bivariate statistics: association between 2
variables
• Multivariate statistics: associations between
more than 2 variables
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IN THIS
COURSE
Part I
Univariate, descriptive
statistics
Part II
Univariate, inferential
statistics
Part III
Bivariate, descriptive and
inferential statistics
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Types of variables
Categorical versus metric variables
• Different types of variables require different types
of statistical methods
• Categorical variables: values are categories
– E.g. hair colour, political party preference, favorite
television show…
– Also known as qualitative variables
• Metric variables: numerical values
– E.g. net income, length, age, number of friends…
– Also known as quantitative variables
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Type of variables:
measurement level
• Categorical variables
– Nominal scale
• Categorical values are unordered
• There is no ‘higher’ or ‘lower, ‘larger’ or ‘smaller’ …
• E.g. gender, eye colour, political party preference…
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Type of variables:
measurement level
• Categorical variables
– Ordinal scale
• Categorical values have a ‘natural’ ordening
• Some values are ‘higher’ or ‘lower’, ‘larger’ or ‘smaller’ …
• E.g. Social class: ‘working class’, ‘middle class’, ‘high class’
• E.g. educational level: ‘no education’, ‘primary education’,
‘secondary education’ and ‘tertiary education’
• E.g. Likert scales: ‘strongly disagree’, ‘disagree’, ‘neither
disagree, nor agree’, ‘agree’, ‘strongly agree’
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• Metric variables
– Interval scale
• Values can be ordered
• Specific numerical distance or interval between values
• How much higher or lower, larger or smaller…
• Values can be added or subtracted
• E.g. Year of birth
• E.g. Temperature in Celsius
Type of variables:
measurement level
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• Metric variables
– Ratio scale
• Can be ordened
• Specific numerical distance or interval between values
• Has a meaningful or true zero point
• Values can be added and subtracted
• Values can be divided or multiplied  ‘ratio’ scale
• E.g. Number of children  0 = no children
• E.g. Age  0 = no age
• E.g. not year of birth  0 ≠ no year of birth
• E.g. Temperature in Kelvin K  0 = no temperature
• E.g. not temperature in Celsius  0 ≠ no temperature
Type of variables:
measurement level
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Natural
order in
values
Specific
numerical
distance
between
values
Can add
or
subtract
values
Can
multiply
or divide
values
True
zero
point
Categorical
1. Nominal
2. Ordinal X
Metric
3. Interval X X X
4. Ratio X X X X X
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• Discrete variable
– A limited set of possible values
– E.g. number of children, hair colour…
– Values such as 0, 1, 2, 3, …
• Continuous variable
– An unlimited continuum of possible values
– Between any two values there is always another possible
value
– E.g. height, age, time…
– Values such as 1, 1.1111, …. , 1.1112, …, 2, …
Type of variables:
Discrete and continuous variables
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• All categorical variables are discrete
• Metric variables could be either discrete or
continuous
Type of variables:
Discrete and continuous variables
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Next week:
Univariate descriptive statistics
Contact:
pieter-paul.verhaeghe@vub.be