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Notes of all Victor Gijsbers philosophy clips (1.1/5.5) University of Leiden

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Universiteit Leiden (UL)

This document contains a summery of all the information given by Victor Gijsbers in his youtube videos on philosophy. These video are part of the course: Wetenschapsfilosophie/Science of philosophy at the University of Leiden

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Geupload op 19 juni 2023
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Type Overig
Persoon Onbekend

philosophy
kuhn
popper
foucault
karl marx
argumentation
universalism
postmodernism
wetenschapsfilosofie
science of philosophy
filosofie

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isabellafiedor Lid sinds 3 jaar 91 documenten verkocht

Clips filosofie

Clip 1.1
Scientists want data and then want to draw conclusions from this data.
Therefore we need to study logic: the study of argumentation. (Is it a good or a bad
argument?).

An argument consists of two parts:
-Premises: what we presuppose
-Conclusion: what we conclude from the premises.

There are two types of arguments:
-Valid argument: an argument where the conclusion really follows from the premises.
-Invalid argument: an argument that is not valid, and thus does not follow from the
premises.

An invalid argument does not mean that a premises used in the argument is not true, it
just is not relevant to the conclusion.

There are two kinds of arguments:
-Deductive argument: an argument in which the truth of the premises absolutely
guarantees the truth of the conclusion.
>If your assumptions are true: your conclusion must be true too
>Has a form from where you can see if it is true or not  background info doesnt matter
-Inductive argument: an argument where the truth of the premises gives good reason
to believe the conclusion, but it does not absolutely guarantee the truth of the
conclusion.
>Makes your conclusion likely, but not certain.
>Most of the time we meet these arguments in science.

Clip 1.2

We will focus on inductive arguments.
 We always need to use background theories.
>Drawing general conclusions from data is always inductive (like: I had 10 dogs that did
not like fish, so all dogs hate fish  so we needed our general knowledge to know that
we can not expect tastes like this).
>Content matters: background info matters.
>Everyone has different background info  therefore different people can draw different
conclusions.

Representative data: Data that represents the subject matter as a whole, and not just
a special part of it.

,Clip 1.3

Common reasoning errors:
1. Confirmation Bias
2. Correlation and causation
3. Probabilities

1. Confirmation Bias

>happens on a daily basis
>Meaning: The tendency to collect and take seriously evidence that supports our beliefs,
and to disregards or ignore evidence that conflicts with our beliefs.
>Whatever someone does, you see it in a certain way because you are biased.
>To be a good scientist: try to have an open mind

2. Correlation and causation

Correlation: the tendency of two things to occur together (smoking + long cancer)
In this case smoking and cancer is also causation  smoking causes cancer.
But it is not always like this, it is very hard to prove causation, so we need to be very
careful to use causable claims.

3. Probabilities

Example: a train accident happened. Was it because of the light that malfunctioned or
the driver that drove through read? A light only malfunctions 1 in a million times, so it
must have been the driver!!  this is an example of a reasoning error based on
probabilities.
>the fact that a hypothisis is unlikely, doesnt mean it is not true.

Clip 1.4

Karl Popper: the most important thing of science was that it was always critical and
never took anything for granted. Scientists should always try to prove their theories
wrong.
 Induction should not be done, because of the background information
 He said scientists do not use iduction only deduction, they NEVER use background info.

According to poper scientists NEVER claim their theory to be true or probable: they know
that there could be a new obeservation that could prove them wrong.
They are only interested in critically testing theories and showing that they are false.
The only conclusions scientists should ever draw, is that conclusions are wrong.

Falsification: observation that shows that a theory is false
Falsified: state of theory that has been shown to be false.
Falsificationism: Popper’s claim that scientists are only interested in falsification.

Most scientists agree that Popper was wrong though  scientists are not only interested
in falsification. And his theory of argumentation was also wrong.
Popper idea’s and ways of logic, also use background information though, therefore he
was wrong.
Conclusion: it is impossible to do without induction in science.

, Clip 2.1 Thomas Kuhn, normal science

Popper’s falsificationism: scientists are always busy trying to prove their own theory
wrong. Being critical is like a definition of being a scientist.

Thomas Kuhn did not agree  most of the time science isnt very critical at all, it only
happens at exeptional moments.

Kuhn’s phases of science
1. Pre-paradigmatic phase
2. Normal science
3. Crisis
4. Scientific revolution

Let’s start with his 1 fase: ‘normal science’
Kuhns key insight: although scientists are usually critical of theories, there is also a lot
that scientists are not critical about. Every scientific discipline, has a huge number of
theories, concepts, measuringments etc that all scientists in that field take for granted.

Paradigm: all the theories, concepts, methods, and so on, that a scientific discipline
takes for granted and that direct research in that discipline.

Kuhn mentions two things about paradigms:
>They are not just taken for granted, people dont even want to be criticle about them!

During the fase of ‘normal science’ people are not interested in being criticle about the
paradigm.
Kuhn points out that this is a good thing though, if a scientists had to question
everything all the time, he could never make much process.

According to Kuhn scientists are not that criticle, they dont question their paradigm.
Kuhn says this is good though, we could never get any work done if we would be too
critical. But then why do we say science is criticle? That, Kuhn says, is because of the
other 3 fases of science.

2.2

According to Kuhn normal science is defined by the existence of a paradigm. A paradigm
consists of all the theories and methods that a scientific discipline takes for granted.
During normal science, scientists trust this paradigm (science as we mostly kno wit).

Fase 2: Pre-paradigmatic phase.
 Before there is a paradigm, these still had to be thought of and made of. The first
linguists and historians did not have theories yet. Scientists in these times had a harder
time, since they did not have a shared scientific vocabulary and did not share common
knowledge about the discipline. It was almost impossible for scientists to work together.
It does not look like science as we know it. According to Khun, this fase of science wasnt
very effective: every scientist was working from scratch. When scientists start to have a
paradigm they enter the ‘normal science’ state.

Anomaly: A problem within the paradigm that scientists are at present unable to solve.
According to Popper, they thus would simply be wrong.

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