Stats Flashcards
1
Q
Logical Errors
A
- Ad Hominem
- Appeal to Authority
- Appeal to Ignorance
- False dichotomy
- Pragmatic fallacy
- Weasel words
- Confusion of correlation and causations
2
Q
Appeal to Authority
A
You believe something is true because someone very important said it
3
Q
Ad Hominem
A
Attacking the researcher for being disreputable instead of the evidence
4
Q
Appeal to Ignorance
A
If you are not certain about your argument, then mine must be true
5
Q
False Dichotomy
A
Considering only the two extremes in a continuum of intermediate possibilities
6
Q
Pragmatic Fallacy
A
Something is true because something else works
7
Q
Weasel Words
A
Use of euphemisms and misleading terminology
- Scientists say that…
- Clinical studies have shown that…
- This medicine may help with…
E.G. Low fat, natural, real fruits, chemical free
8
Q
Transparency and skepticism
A
- Challenge existing theories
- Peer review
9
Q
Authority vs Theories vs Evidence
A
- Should not rely on authorities
10
Q
Confusion of Correlation and Causation
A
Since two things go together, one must have led to the other
11
Q
Constructs/Concept
A
- Hypothetical description of something that is not real
E.G. Intelligence, anxiety, motivation
12
Q
Pre-scientific Constructs
A
- Cold and hot “energy”
- Spirit forces
- A pinch (of salt)
13
Q
Scientific Constructs
A
- Heat energy
- Time in seconds
- Gram
14
Q
Conceptual Definition
A
- Describing a construct in terms of what it is and what it is not
- How it might relate to existing theories
15
Q
Reification
A
- AVOID
- How someone’s personality reacts to the world
E.G. people belieing a certain gambling machine has greater luck
16
Q
Falsifiability
A
- CONSIDER
- If you create something that cannot be measured, there will never be any way to tell if its real
E.G. There are fairies in my garden
17
Q
Operational definition of construct
A
- How the construct is measured
E.G.
Motivation = Rate of button pressing
Memory = Number of Things Recalled
Learning = Decrease in Time to Solve Puzzle
18
Q
Problems with operational definition of construct
A
- Operational definition is not a construct
- Finding a way to measure it does not make it real
19
Q
Self-Report
A
- Are you racist?
- People can answer dishonestly
20
Q
Social Desirability Scale
A
- How likely will respondents give answers that sound good instead of answers that are true
21
Q
Direct Measures
A
- What’s your favourite painting
22
Q
Indirect Measures
A
- Warmness of carpet at specific paintings
23
Q
Anecdotes
A
- Interpreted stories of a single occurrence in the past
- Theory and evidence is mixed together
E.G. I was sick so I did X and now I’m better so X made me better
- There were so many other factors that could make you feel better
24
Q
Case Studies
A
- Theory and evidence are separated
- Identifying the specific factor that caused you to feel better
25
Correlational Studies
- At least two variables are measured from each person to find relationship between variables
26
CORRELATION DOES NOT MEAN CAUSATION
If X correlates with Y, it could be either
- X → Y
- Y → X
- Z → X or Z → Y
27
Correlation Coefficient: +
As values on one variable get bigger, values on the other get bigger
28
Correlation Coefficient: -
As variables on one variable get bigger, values on the other get smaller
29
Independent Variable
The presumed caused and what is manipulated
30
Dependent Variable
What is measured
31
Random Allocation
Participants are given an experimental condition at random
32
Random Selection
Participants are chosen randomly from a population
33
Replication
Study is repeated with same method and same results are produce
34
Blinding
Single Blind
- Participants are unaware of which condition they are in
Double Blind
- Participants are unaware of which condition they are in
- Researchers are unaware which condition is being run
35
Internal Validity
How certain we are that changes in the IV caused changes in the DV
36
External Validity
Extent to which findings from the study can be generalised to the population at large
37
True Experiment
- All independent variables are randomly allocated
- Controlled variables
- Establish cause and effect
38
Quasi Experiment
- Less control over variables or no control variable
- Casual inference
- Lower internal validity but higher external validity
39
Theory of Cognitive Dissonance
A person comes to believe in what they do to reduce internal conflict
40
Alternative Hypothesis
Suggests there is a relationship or difference
41
Null Hypothesis
Suggests that there is no relationship or difference
- If we reject the null hypothesis → We have evidence of an effect
- If we retain the null hypothesis → We have no evidence of anything
42
Experimental Hypothesis
Explicitly defines the expected change or effect
43
Mode
Most frequent score
- Advantages: Unaffected by extreme values
- Disadvantages: if all cancers are grouped together cancer is the most common way to die, but if separate cancers are considered road fatalities have a higher rank
44
Median
Middle score
- Avantages: Unaffected by extreme values
- Disadvantages: Not based on all values
45
Mean
Average score
- Advantages: Takes all scores into account
- Disadvantages: Affected by extreme values
46
Standard Deviation
1. Count the number of scores
2. Add up the scores and find their mean
3. Find the deviation scores for every score
4. Square every deviation sore then add them up
5. Divide by the number of squares
6. Take the square root
47
Inferential Statistics
- Population
- Sample
48
Sampling Distribution
- Simulate an experiment being run over and over again
- Shows variability across experiments
- Shows likelihood of obtaining a result if the null is true
49
Raw Score Distribution
- Shows variability across individuals
- Shows likelihood of obtaining a particular score
50
Inferential Statistics: POPULATION
- Entire collection in which you are interested
- Parameters
51
Normal Distribution
Two thirds of all scores fall within one standard deviation of the mean
52
Inferential Statistics: SAMPLE
- Selection from the entire collection
- Statistics
53
P-Value
Probability of results occuring due to random chance
If the probability is high we retain the null hypothesis
- If p>0.05 retain
If the probability is low we reject the null hypothesis
- If p<0.05 reject
The lower the p, the better, since less likely to have occurred by random chance
54
Probabilistic Nature of Conclusions
See docs (table)
55
Statistical Significance
How likely the effect was due to chance
56
Practical Significance
- How useful is the effect
- Is it worth investigating further
57
Power
- Probability of finding an effect (rejecting the null hypothesis while it is false)
58
Type 1 Error
- Probability that the null hypothesis is incorrectly rejected when it is actually true
- Detecting an effect when there isn't one
59
Type 2 Error
- Probability that the null hypothesis is retained when it is actually false
- Fail to detect an effect when there is one
60
Denialism
- Finding a few imperfections in a theory and disproving every aspect of the theory
