Extensive summary of all lectures for the course Statistics. Including all things gone over during lectures, extra explanations of how to use R and extensive explanations of the terminology. We used the book 'Learning Statistics with R' by Danielle Navarro for this course. Passed the course with an...
Summary written by Saskia Kriege
Cognitive Science and Artificial Intelligence
Including all lectures from the whole semester, Lecture 1 – 12
,Lecture 1 – Introduction
Generating and Testing Theories
Theory =
Hypothesized general principle or set of principles that explains known findings about
a topic and from which new hypotheses can be generated
Hypothesis =
Prediction typically derived from theory/obersvation
Falsification =
Act of disproving a theory or hypothesis
Scales of measurement
Categorical =
Divided into distinct categories
- Binary → two categories
- Nominal → more than two categories
- Ordinal → same as nominal, but categories have logical order
Continuous =
- Interval → equal intervals on the variable represent equal differences in
property being measured
- Ratio → same as interval, but ratios of scores on the scale must also make
sense and have a true 0 value.
,Reliability of our measures
Reliability =
Ability of the measure to produce same results under same conditions
Test-retest reliability =
Ability of a measure to produce consistent results when the same entities are tested
at two different points in time
Inter-rater reliability =
Consistency across people, do they produce same answer?
Parallel Forms Reliability =
Do different measures that are supposed to measure the same thing actually
measure it the same?
Internal consistency reliability =
Do things that are supposed to measure the same thing actually measure it?
Common Types of Research
Correlational research =
Observing what naturally goes on in the world without directly interfering with it
Cross-sectional research =
Data come from people at different age points, with different people representing
each age point.
Could be quasi-experimental, case study, naturalistic observation
Experimental research =
One or more variable is systematically manipulated to see their effect on an outcome
variable.
Randomization
Statements can often be made about cause and effect
Be careful for:
- Confounds → unmeasured variable that could be related to the variables of
interest
- Artefacts → something that might threaten the external validity or construct
validity of your results
, Types of Validity
Internal validity =
Extent to which you are able to draw the correct conclusion about the causal
relationships between variables
External validity =
Generalizability of your findings. To what extent do you see the same pattern of
results in ‘real life’ as you saw in your study
Construct validity =
Whether you are actually measuring what you want to be measuring
Face validity =
Whether or not a measure ‘looks like’ it is doing what it is supposed to do
Ecological validity =
Entire set up of the study should closely approximate the real world scenario that is
being investigated
Lecture 2 – Introduction to R
& = and
| = or
! = not
If you don’t give an argument to R, it uses the default values (i.e. values that are
given automatically, for example rounded to 0 decimals)
Variables =
Used to store information, use <- to create
- Numeric variables → store numbers
- Character variables → store text (“bob”)
- Logical variables → TRUE/FALSE
Vectors =
Store multiple pieces of information
Create using c(), extract specific elements using [ ]
Data frames =
Way R stores a typical data set
Collection of variables ‘bundled’ together
Each row is a case
Each column is a variable
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