Lesson 3 Flashcards

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1
Q

mean and standard deviation only work well for ______ ________ ____.

A

Normally distributed data

- data can become skewed - mean and standard dev. don’t work as well

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2
Q

Why can skewness be problematic?

A
  • can pull the mean away
  • not robust to extreme values
  • data can be skewed left or right
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3
Q

What is modality? How is it challenging?

A

mode = value that appears most often

  • shows peak
  • can have more than one peak (uni/bi/multimodal)
  • challenging for researchers - what is the typical value?
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4
Q

Why are research methods necessary?

A

Good research design is incredibly important, for example the prefrontal lobotomies that turned people into vegetables for no goof reason

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5
Q

What is descriptive research? (under observational research)

A
  • describes aspects of a phenomenon, population, etc.
  • tries not to interefere with how it’s arisen
  • just cataloguing things
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6
Q

What is correlational research? (under observational research)

A
  • looks for statistical relationships between variables, phenomena, etc., make generalizations
  • finding assocaiations between varaibles
  • often uses methods of descriptive research to gather the data
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7
Q

What is experimental research

A
  • attempts to manipulate variables to find casual roles between them
  • critical difference between this and observational research (causes, other than just predicts)
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8
Q

What is naturalistic observation?

A

The recording of behaviour without trying to manipulate it

  • eg. warfare is not distinctly human
  • limited - can’t make casual statements, but can clue you into new hypothesis
  • obseravtions in real world; high external validity (findings are valid in real-world settings)
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9
Q

What is reactivity?

A
  • observations can effect behaviour
  • if you wait long enough, will subside naturally
  • some methods can help disguise, reduce reactivity caused by prescence
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10
Q

Describe case studies

A
  • weakest of all research designs
  • examines one or a few people in great detail, typically over a long period of time
  • useful for existence proofs (demonstration that a given phenomenon can occur)
  • doesn’t control variables, so it’s not ctually “proof”
  • can be highly subjective
  • useful for very rare or unusual phenomenon difficult to study in the laboratory
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11
Q

What is the fundamental flaw with anecdotes?

A
  • short, personal experience
  • used as evidence in arguments
  • does not establish causation
  • no control over other possible reasons, not necessairily able to be generalized - survival bias
  • may not be representative
  • may be ignoring contradictory claims
  • *are not evidence
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12
Q

Describe the research methods of surveys and self-reports. What are some of its fundamental flaws?

A
  • questionnaires, interviews
  • easy to collect large amounts of data on numerous factors
  • does not establish causation between variables
  • doesn’t allow much explanation as to why - can hint at possible causes
  • can be massively influenced by wording
  • there is an assumption that the person understands the question being asked
  • assumption that the person has insight and are being honest
  • positive impression management - trying to make themselves look better
  • malingering - trying to appear disturbed in a way
  • lapses in memory/biases can affect responses
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13
Q

Describe random sampling.

A
  • important to consider how generalizable the data is
  • ensures every person in a population has an equal chance of being chosen to participate
  • need to consider the method in which it is administered
  • eg. email doesn’t include as many senior citizens
  • required for generalizable results in any form of research
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14
Q

What is reliability in scientific testing?

A
  • consistency of measurement
  • test re-test reliability
  • if you re-test, should produce same or similar results - shoudl be able to replicate
  • interobserver reality
  • two people taking the same test should arrive at the same conclusion
  • also called “interrater reliability”
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15
Q

What is validity in scientific testing?

A
  • the extent to which a system measures what it says it does

- eg. lie detectors don’t detect lies, detect physiological responses

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16
Q

Describe correlational designs

A
  • used to examine the relationship between variables
  • relies on the “correlation coefficient”
  • a measure to which the degree in which two variables are related
  • closer to 1/-1, more drastic
  • denoted as p (greek rho) or “r”
  • positive - increase in x and y
  • negative - decrease in x and y
  • 0 - not relationship
  • unless you have a perfect correlation, there will always be an exception
17
Q

Why can’t we do research methods intuitively?

A
  • people are notoriously bad at estimation
  • illusionary correlation - perception of correlatioon where there is none
  • confirmation bias - seek out only infomration that supports your expectations
  • availability heuristic - estimate likelihood depending on how easily it comes to mind
  • correlation does not equal causation
  • the third variable problem - two variables may be related to one another because they are both casually related to a third variable
18
Q

How do you find causation?

A
  • observational - too many factors, cannot determine causation
  • experimental - less variable factors - results are more likely to be due to the manipulated variable
  • means are useless without the variation around them
  • can use error bars
    confidence interval
  • why it’s important to replicate studies
19
Q

Why does data fluctuate?

A
  • impossible to control all aspects of an experiment (either practically or ethically)
  • generates unsystematic variation (aka error) in the measurement
  • error bars can represent different things
20
Q

What is statistical significance?

A
  • probability of obtaining the result is less than a pre-designed threshold
  • does not mean it’s practically significant
  • with a large enough sample size, all findings are statistically significant
21
Q

List some hazards in experimental design

A
  • random assignment is not used (randomly sorting subjects into the experiment’s groups)
  • assumes that uncontrolled differences are evenly distributed
  • don’t confuse with random sampling