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  2.  
  3. Tilburg University (UVT)
  4.  
  5. Statistics 1 (822187B6)

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Summary Statistics I
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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...

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  • 14 december 2020
  • 64
  • 2019/2020
  • Samenvatting

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Voorbeeld van de inhoud

Statistics I, Summary

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)

Remove NA’s → na.rm = TRUE
Create function → name_function <- function(…)
fahrenheit_to_celsius <- function(temp_F) { temp_C <- (temp_F - 32) *
return(temp_C) }

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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