Test 2 - PHIL105

Question 1

Specific Claims - Definition

Answer 1

Claims about particular instances of things

Question 2

Universal Claims - Definition

Answer 2

Claims about entire classes of things

Question 3

Inductive Generalisations - Definition

Answer 3

Making universal claims based on specific claims

Note: Inductive Arguments = not valid since premises do not guarantee conclusion (unless complete enumeration)

Question 4

Deductive reasoning - Definition

Answer 4

Making specific claims based on universal instances

Question 5

Benefits of Inductive Generalisations:

Answer 5

Helps us figure out how things are are going to be, based on how things have been in the past

Question 6

Problems with Induction

Answer 6

Justification for using induction is itself an inductive argument

Argument in favour of using induction is circular (begs the question)

Question 7

Logical Asymmetry?

Answer 7

Any no. of positive instances of a universal claim cannot guarantee its truth (unless you have every possible instance of the universal claim)

Thus, one negative instance of a universal claim is enough to show it to be false

Question 8

Complete Emuneration

Answer 8

When entire class of things is small and accessible, all possible claims can be checked

Question 9

Three rules for good induction

This is the exam question - “three rules for good generalisation”

Answer 9

1. Sample should be sufficiently numerous and various
2. Should look for disconfirming as well as confirming cases of generalisation
3. Consider whether a link b/w the two classes is plausible, given other knowledge that we possess

Question 10

Causal Claims - Definition

Answer 10

Assertions that events of one type are followed by events of another type

Question 11

Two types of causal claims:

Answer 11

General: A causes B
Particular: A caused B

Question 12

General causal claim:

Answer 12

Means that another event of the same kind as A, would, in similar circumstances, produce another event of the same kind as B

Question 13

Particular causal claim:

Answer 13

Presupposes a general causal law (i.e. shirt is stained because I spilt coffee on it)

Question 14

Mills Methods: All 5

Answer 14

1. Method of Agreement
2. Method of Difference
3. Joint Method of Agreement & Difference
4. Method of Residues
5. Method of Concomitant Variation

Question 15

Method of Agreement

Answer 15

Look for common factor that is present in all cases in which effect occured

Question 16

Method of Difference

Answer 16

Look at antecedent circumstances when E (event) occurs and compare these to antecedent circumstances where E fails to occur

Question 17

Joint Method of Agmt and Diff

Answer 17

Requires that there be some Agreement, but also that there is at least one different case where the proposed cause isn’t present and where effect is also not present

Question 18

Method of Residues

Answer 18

If we already know cause of part of the effect, we can subtract that to figure out what causes the rest of the effect

Question 19

Method of Concomitant Variation

Answer 19

If quantitative changes in a phenomenon are associated with quantitative changes in another phenomenon - likely causal connection b/w them.

Idea = change in the strength of the effect should correspond to a change in the strength of the cause

Question 20

Limitations of Mills Methods

Answer 20

Does not tell us how to determine whether the antecedent circumstances have been properly analysed

Does not tell us which antecedent circumstances to investigate

Can never provide a certain, demonstrative, or conclusive proof of a causal claim

Does not provide a mechanical or automatic way to discover causal connections.

Question 21

Necessary v Sufficient Conditions

Answer 21

Necessary = condition must have occurred to guarantee result
Sufficient = conditions is enough to guarantee result

Question 22

Necessary v Sufficient: Method of Agmt & Diff

Answer 22

Agmt: provides evidence that the proposed cause is sufficient for the effect
Diff: gives us necessity

Question 23

Statistical Propositions

Answer 23

Propositions that present quantitative evidence about a category of things

Question 24

Statistical Properties can be divided into two categories … those being?

Answer 24

Values: numerical measure or category to which particular variables can be assigned

Variable: a kind of property a thing can have

Question 25

Variables can be

Answer 25

Qualitative or Quantitative

Question 26

``` Types of Statistical Info: Definitions Totals Ratios Frequency Distribution Average (mean) Median ```

Answer 26

Totals; adding up a set of units Ratios; proportion of total ``` Frequency; how many things in a class have a certain property - Have absolute (actual number of P's that are Q's) and relative (proportion of P's that are Q's) ``` Distribution; how many things have each property (requires categories) Average (mean); central value of S's on a quantitative variable Median; Central value in set of quantitative values (middle value)

Question 27

Average v Median

Answer 27

Average = Sensitive to extreme values in ways that medians are not Note: General rule - if you only have extremes in one direction, the median is often a better measure

Question 28

Selection Bias

Answer 28

Bias introduced by selection of individuals, groups or data for analysis in such way that proper randomisation is not achieved

Question 29

Testing Bias

Answer 29

- Occur due to the way questions are worded/phrased or even the questions themselves are presented - Participants can easily be primed so that they are more likely to answer one way or another

Question 30

Priming

Answer 30

Phenomenon whereby exposure to one stimulus influences a response to a subsequent stimulus, without conscious guidance or intention

Question 31

How to generalise from a sample: (steps)

Answer 31

1. Was MoE reported? If so, how large? 2. How was the sample selected? Was it done in such a way that we can ensure it's representative? 3. How was info about the sample acquired? Any suggestions of unreliability

Question 32

Correlation: Defintion

Answer 32

Specific type of relationship b/w two variables

Question 33

Correlation & Causation:

Answer 33

KEY: Correlation does not imply causation!

Question 34

Experimental v Observational Studies: Note

Answer 34

Although can control some variables, we cannot control them all, due to either: (a) the inability to control said variable (b) controlling the variable would be wildly impractial or immoral Therefore, need to rely on an observation of things that exist.

Question 35

Observational Studies - Note?

Answer 35

With these studies, we need to rely on observation of things that already exist, that do not involve random assignment to control and experimental groups. Thus, if a correlation is found, may be due to a 3rd unknown variable = CONFOUNDING VARIABLE

Question 36

Internal Validity

Answer 36

How sure we can be about cause & effect in regard to the actual numbers of the group that was studied

Question 37

External Validity

Answer 37

Whether the generalisation holds for a wider group

Question 38

Proxy variables

Answer 38

Sometimes we use other variables as a proxy for what we wish to measure However, if you find a correlation b/w a cause and a proxy variable, need to assume that the proxy is in fact a good measyre of the variable you are interested in

Question 39

Statistical Fallacies: Examples?

Answer 39

Mistaking correlation for causation Gamblers Fallacy Mistaking statistical significance for clinical significance Base rate fallacy

Question 40

Conjunction Fallacy

Answer 40

thinking that the conjunction of two events is more likely than a single general event

Question 41

Mistaking statistical significance for clinical significance

Answer 41

Stat. signif. means that there is a low probability results are due to random chance

Question 42

Base Rate Fallacy

Answer 42

When presented with base-rate info (general) and info about a specific case, we tend to ignore the general info and focus on the specific case

Question 43

Truth Inflation: Two effects

Answer 43

File-drawer Effect: - Researchers who do not find any effect just put on their work away and never submit for publication Publication Bias: - Research reporting that there is some effect is far more likely to be published than research that shows there is no effect Conc: - end up with much higher probability that a statistically significant effect reported was just due to random chance

Question 44

Repression to the Mean:

Answer 44

IF first measurement of an effect is extremely high or low compared ti the mean, it will usually be closer to the mean on a subsequent measurement and vice versa This is simply due to natural variation.

Question 45

Demarcation Problem

Answer 45

How to distinguish science from non-science from pseudoscience?

Question 46

Characteristic Features of Pseudoscience

Answer 46

a) Hostility towards scientific criticism b) Trying to move 'burden of proof' away from themselves c) Rendering claims unfalsifiable d) Claims easy solutions for complex problems e) Failing to consider all hypotheses f) Fundamental principles are often based on a single case g) Making a virtue of ignorance h) Working backward from a conclusion i) Cherry picking data j) Failure to challenge core assumptions k) Failure to engage with scientific community l) Utilising scientific-sounding but ultimately meaningless language m) Claiming to be many years/decades ahead of the current research community n) Rely on poor forms of evidence

Question 47

Standard Forms:

Answer 47

``` Universal Assertion (All S are P) Particular Assertion (Some S are P) ``` ``` Universal Denial (No S are P) Particular Denial (Some S are not P) ```

Question 48

Contraries v Contradictories

Answer 48

``` Contraries = Do NOT Exhaust logical space Contradictories = DO exhaust logical space ```

Question 49

Connectives

Answer 49

``` Negation = Not = ~ Conjunction = And = & Disconjunction = Or = v Conditional = if ... then ... = horseshoe Biconditional = If & only if = three lines horizontal ```