Philosophy of science exam questions + pt 2 Flashcards
(37 cards)
Define the value-free ideal of science.
According to the value-free ideal, good science should never rely on, or be influenced by, human
values like moral, social or political beliefs in assessing the evidence for scientific hypotheses.
Normative claim
a claim that asserts that such-and-such OUGHT to be the case
A scientific activity or project
Aims to create explanations about
Puts forward ideas that can be tested empirically
Updates ideas based on reliable evidence
Would abandon any idea that was thurroughly refuted
Applies mathematical tools when they are appropriate
Involves the broader scientific community
How do you know which
similarities and idealizations matter
for learning about a target
robustness analysis:
Build slightly different models of same target
Manipulate the models in comparable ways
Compare models results
Blind monks examining an
elephant, an ukiyo-e print by
What’s the point of robustness? (model)
Assess sensitivity of a model to
changes in its basic structure
Identify model features responsible for
certain results
Evaluate what similarities and idealizations
matter to learning about the world
Dont trick yourself you dont understand lecture 7 at all so youre gonna have to do that at some other point without notecards
Abduction
Inference to the best possible explanation
Hans engages in behaviours
y. The best scientific explanation for an
individual engaging in behaviours y is that
the individuals can do arithmetic. Therefore, Hans can do arithmetic.
the ratio between your stake X (= how much you could lose) and
he total amount staked X + Y
You believe that a 3:1 odd is fair for a bet on the
truth of h (e.g., that you will pass this course).
If you bet that h is true, you win €1 (and get your
stake in return) if you are right, and lose €3 if wrong.
Your degree of belief in h is 3/(3 + 1) = 3/4
Andrey Kolmogorov’s axioms of probability
Axiom 1: All probabilities are numbers
between 0 and 1
* Axiom 2: If a proposition is certainly true,
then it has a probability of 1. If
certainly false, then it has prob. 0.
* Axiom 3: If h and h* are exclusive
alternatives (they cannot both be
true at the same time),
then P(h or h) = P(h)+P(h)
Dutch book
If your degrees of belief do not conform to the rules of probability, there are possible betting situations where you are guaranteed
to lose money (you fall prey of a Dutch book).
Frequency interpretation of probability
The probability of an outcome is the
frequency with which the outcome
occurs in a long sequence of trials.
Problem of single-case probabilities
Cannot assign probabilities to one-off events…
Propensity interpretation of probability
The probability of an outcome is a propensity inherent in the physical
conditions producing the outcome.
E.g.
Being fragile is a causal disposition of glass
Null Hypothesis Significance Testing
- Formulate a null hypothesis e.g., This treatment is not effective
- Develop expectations in the form of probability distributions for possible
outcomes given the truth of hypothesis
E.g., if this treatment is not effective, then when I run an experiment, such and such
differences between control and treatment groups will be observed
- Gather data/observations & Evaluate to what degree observed data violate expectations
- Draw an inference from this comparison E.g., the data disprove the idea that the
treatment makes no difference
Basic idea of null hypothesis testing
Null Hyp (p) leads one to expect a certain range of possible outcomes (if p, then q)
When observed data are far outside that range (not-q), then we can reason such
data would be very unlikely if Null is true» data provide grounds for rejecting Null (not-p)
NOTE When fail to reject Null, that does not mean that Null is true
Significance level
Decision about how improbable, given
the truth of the Null, an observed result
must be to warrant rejecting Null
p-value
a number describing how likely it is that your data would have occurred under the null hypothesis of your statistical test.
The Bayesian approach (method)
1) formulate hypothesis (e.g. H1: I am ill, H2: I am healthy
2) Assign a probability to each outcome
3) Gather data (e.g. test)
4) Evaluate the degree to which the data (dis)confirms the Hypothesis
5) Update probability of the hypothesis
Bayes theorem
P(HlO) = P(OlH) * P(H)/P(O)
P(OlH): Conditional Probability of O given that H is true
Advantages of Bayes theorem
- Allows us to account for our previous knowledge of the world (priors)
E.g., including information about prevalence of the disease2. Allows us to check how much the data confirms or disconfirms a hypothesis Much better than just rejecting H03. Informs us on how to adjust our beliefs in the different hypotheses
Why bother about causation
To intervene and predict, so that we can stop bad things happening and make more good things happen
To explain why or how things happen
Two variables C and E are causally related when
if the value of C changed,
the value of E would change too
Causal Markov Condition
CMC) says that each
variable in a graph is independent of every other
variable (except its effects) conditional on all of its
direct causes
Value-Free Ideal suggests that science is
a source of objective knowledge to the extent it is free from values
