Philosophy of Science and Statistics
Max Batstra
March 29, 2026
1
, 1
Science
2
,Chapter 1 What is philosophy of science?
The central question of the philosophy of science is: ”What is science? ”
The questions that arise from this central question are:
1. What distinguishes science from pseudoscience and from non-scientific fields?
2. What is the relationship between scientific theories and reality?
3. How do the sciences evolve?
Philosophy is a way of thinking:
• Rationally
• Radically critical
• With broad scope
The importance of philosophy of science is to clarify concepts, to critically evaluate assumptions and to put
findings in a broader context.
Chapter 2 Predictable irrational
Critical thinking is rational and autonomous thinking. According to Braeckman, we can set our thinking
straight by using the following three rules of thumb:
1. Do not accept a claim merely because it sounds plausible
Having the feeling of understanding or knowing something is by no means a guarantee that you actually
understand or know something.
2. Use Occam’s razor
Occam (English philosopher) tells us that the most economical/parsimonious explanation (simple,
concise and avoids unnecessary complexity or additional assumptions) is often the best.
3. Be aware of cognitive illusions or biases (see below)
There are three categories of reasoning errors:
1. General reasoning errors
• Confirmation bias
The tendency to register and look for information that confirms our beliefs, whilst remaining
almost entirely blind to information that contradicts our beliefs.
• Irrational cognitive dissonance reduction
When information we gather from the world contradicts our beliefs, we tend to interpret that
information in such a way that it no longer contradicts our beliefs.
• Dunning-Kruger effect
The tendency for non-experts to overestimate their knowledge of something and of experts to
underestimate their knowledge.
3
, • Bias blind spot
Detecting reasoning errors much more easily in the reasoning of others than in our own reasoning.
• Self-overestimation
We overestimate our own talents and prospects in life.
• Belief bias
Accepting the validity of an argument simply because the conclusion sounds plausible or because
you agree with the conclusion.
• Hindsight bias
After events took place, we usually thing that our predictions about that event were in the past,
were better than they actually were.
• Stereotyping
Expecting an individual of a particular group to have certain characteristics associated with the
group, without having information about that person.
2. Reasoning errors of investors/ consumers
• Choice supportive bias
We remember the choices we made in the past as being better than they actually were.
• Endownment effect
We accord more value to something, simply because we own it.
• Bandwagon effect/ ingroup bias
We adopt beliefs more quickly when the come from people in our group and blindly follow the
behaviour of our group.
• Anchoring
A given piece of information can strongly influence our estimates. See example on page 20-21
about trees. Anchoring is a form of the Framing effect.
• Framing effect
Drawing different conclusions from the same information, because it is presented differently. Recall
example about the Economist magazine in (page 58).
• Loss aversion
We feel the negative impact of a loss more intensely than the positive impact of a gain of the same
size.
• Sunk cost fallacy
Taking into account incurred and non-recoverable costs in deciding wheter to continue with a
project (and thus continue to invest in it).
3. Statistical/ mathematical reasoning errors
• Statistical reasoning errors
Intuitively we perform poorly at estimation probability.
• Base rate fallacy
We tend to ignore base rates in estimating the probability that something will occur. In general,
we often turn a blind eye to general, implicit information and focus exclusively on specific, explicit
information.
4
, • Availability bias
We overestimate the likelihood that something will occur when it is easy to recall or imagine.
• Gambler’s fallacy
Expecting a statistical correction when that expectation is not justified. For example after tossing
heads two times, expecting the third time must be tails.
• Hyperactive pattern detection
Seeing patterns in random series. For example:
a hominid sees a bush move and hears it rustle. It can either be the wind or a predator stalking
in the bushes. Thinking that it is the wind while it is a predator (a false negative) is a lot more
costly than making the opposite mistake (a false positive). So, again, we are inclined to make too
many false positive mistakes to avoid the costly false negative mistakes
• Exponential reasoning errors
We underestimate exponential growth, because we’re used to linear growth.
We are equipped with a thinking system that is both fast and economical. It works automatically, quickly,
and intuitively. Daniel Kahneman calls this cognitive mechanism system 1. We also have a system that can
check the output of system 1 and overwrite it, if necessary. This system 2 is slow, conscious, and requires
effort.
• System 1
– Fast, automatic, and intuitively. Often based on heuristics (mental shortcuts or rules of thumb)
and patterns derived from past experiences.
– Prone to biases and cognitive shortcuts
– Less demanding in terms of mental effort and always active.
• System 2
– Slow and requires conscious effort
– Involves logical reasoning, careful evaluation of information and complex problem solving. Essen-
tial for unfamiliar or complex tasks and deliberate decision making.
– It can override system 1 if necessary, allowing for more careful and reflective considerations of
options.
In order to guard our thinking against reasoning errors, we must first be aware of the three sources of
reasoning errors:
1. Intuitive thinking of system 1 System 1 is prepared to make a large number of mistakes to avoid costly
mistakes (this is called ’error management’). It causes us to see patterns or causalities that do not
exist (hyperactive pattern detection for example). The heuristics of system 1 have been designed to
guide us in the environment in which we have spent the vast majority of our evolutionary history.
2. Emotions
Natural selection has provided us with emotions, because it is only interested in action. In order to act
you need a belief (cognitive component) and a desire (affective component). The affective component
is the driving factor of action, and the cognitive component is the guiding factor. The affective, in
particular, influences the cognitive, which is known as the affect heuristic. This causes us to make
decisions based on our emotions. Our ‘ingroup - outgroup bias’ stems from this heuristic as well.
5
Max Batstra
March 29, 2026
1
, 1
Science
2
,Chapter 1 What is philosophy of science?
The central question of the philosophy of science is: ”What is science? ”
The questions that arise from this central question are:
1. What distinguishes science from pseudoscience and from non-scientific fields?
2. What is the relationship between scientific theories and reality?
3. How do the sciences evolve?
Philosophy is a way of thinking:
• Rationally
• Radically critical
• With broad scope
The importance of philosophy of science is to clarify concepts, to critically evaluate assumptions and to put
findings in a broader context.
Chapter 2 Predictable irrational
Critical thinking is rational and autonomous thinking. According to Braeckman, we can set our thinking
straight by using the following three rules of thumb:
1. Do not accept a claim merely because it sounds plausible
Having the feeling of understanding or knowing something is by no means a guarantee that you actually
understand or know something.
2. Use Occam’s razor
Occam (English philosopher) tells us that the most economical/parsimonious explanation (simple,
concise and avoids unnecessary complexity or additional assumptions) is often the best.
3. Be aware of cognitive illusions or biases (see below)
There are three categories of reasoning errors:
1. General reasoning errors
• Confirmation bias
The tendency to register and look for information that confirms our beliefs, whilst remaining
almost entirely blind to information that contradicts our beliefs.
• Irrational cognitive dissonance reduction
When information we gather from the world contradicts our beliefs, we tend to interpret that
information in such a way that it no longer contradicts our beliefs.
• Dunning-Kruger effect
The tendency for non-experts to overestimate their knowledge of something and of experts to
underestimate their knowledge.
3
, • Bias blind spot
Detecting reasoning errors much more easily in the reasoning of others than in our own reasoning.
• Self-overestimation
We overestimate our own talents and prospects in life.
• Belief bias
Accepting the validity of an argument simply because the conclusion sounds plausible or because
you agree with the conclusion.
• Hindsight bias
After events took place, we usually thing that our predictions about that event were in the past,
were better than they actually were.
• Stereotyping
Expecting an individual of a particular group to have certain characteristics associated with the
group, without having information about that person.
2. Reasoning errors of investors/ consumers
• Choice supportive bias
We remember the choices we made in the past as being better than they actually were.
• Endownment effect
We accord more value to something, simply because we own it.
• Bandwagon effect/ ingroup bias
We adopt beliefs more quickly when the come from people in our group and blindly follow the
behaviour of our group.
• Anchoring
A given piece of information can strongly influence our estimates. See example on page 20-21
about trees. Anchoring is a form of the Framing effect.
• Framing effect
Drawing different conclusions from the same information, because it is presented differently. Recall
example about the Economist magazine in (page 58).
• Loss aversion
We feel the negative impact of a loss more intensely than the positive impact of a gain of the same
size.
• Sunk cost fallacy
Taking into account incurred and non-recoverable costs in deciding wheter to continue with a
project (and thus continue to invest in it).
3. Statistical/ mathematical reasoning errors
• Statistical reasoning errors
Intuitively we perform poorly at estimation probability.
• Base rate fallacy
We tend to ignore base rates in estimating the probability that something will occur. In general,
we often turn a blind eye to general, implicit information and focus exclusively on specific, explicit
information.
4
, • Availability bias
We overestimate the likelihood that something will occur when it is easy to recall or imagine.
• Gambler’s fallacy
Expecting a statistical correction when that expectation is not justified. For example after tossing
heads two times, expecting the third time must be tails.
• Hyperactive pattern detection
Seeing patterns in random series. For example:
a hominid sees a bush move and hears it rustle. It can either be the wind or a predator stalking
in the bushes. Thinking that it is the wind while it is a predator (a false negative) is a lot more
costly than making the opposite mistake (a false positive). So, again, we are inclined to make too
many false positive mistakes to avoid the costly false negative mistakes
• Exponential reasoning errors
We underestimate exponential growth, because we’re used to linear growth.
We are equipped with a thinking system that is both fast and economical. It works automatically, quickly,
and intuitively. Daniel Kahneman calls this cognitive mechanism system 1. We also have a system that can
check the output of system 1 and overwrite it, if necessary. This system 2 is slow, conscious, and requires
effort.
• System 1
– Fast, automatic, and intuitively. Often based on heuristics (mental shortcuts or rules of thumb)
and patterns derived from past experiences.
– Prone to biases and cognitive shortcuts
– Less demanding in terms of mental effort and always active.
• System 2
– Slow and requires conscious effort
– Involves logical reasoning, careful evaluation of information and complex problem solving. Essen-
tial for unfamiliar or complex tasks and deliberate decision making.
– It can override system 1 if necessary, allowing for more careful and reflective considerations of
options.
In order to guard our thinking against reasoning errors, we must first be aware of the three sources of
reasoning errors:
1. Intuitive thinking of system 1 System 1 is prepared to make a large number of mistakes to avoid costly
mistakes (this is called ’error management’). It causes us to see patterns or causalities that do not
exist (hyperactive pattern detection for example). The heuristics of system 1 have been designed to
guide us in the environment in which we have spent the vast majority of our evolutionary history.
2. Emotions
Natural selection has provided us with emotions, because it is only interested in action. In order to act
you need a belief (cognitive component) and a desire (affective component). The affective component
is the driving factor of action, and the cognitive component is the guiding factor. The affective, in
particular, influences the cognitive, which is known as the affect heuristic. This causes us to make
decisions based on our emotions. Our ‘ingroup - outgroup bias’ stems from this heuristic as well.
5
