PSYC 102 Lecture Notes - Lecture 1: Richard Feynman, Naturalistic Observation, Confirmation Bias
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Introduction – Jan 5
definition:
- scientific study of BMP
- how we think (cognitive), feel (emotional), and act (behavioural)
goals:
- describe explain predict control
methods:
- understanding = may methods converge to same conclusion
2 basic questions:
1. how we know what we know
2. how we learn what we do not know
reasoning:
- regression to the mean = any spike will level out in time
- leads to the assumption of causation where there is none
- we cannot know what causes what, without carefully controlled studies
- personal experience does NOT = valid evidence
Scientific Method – Jan 7
Our best protection against sloppy thinking & human reasoning unfailing method
Common Traps
1. correlation NOT = causation (think about all plausible reasons for correlation)
2. order in random events: random data, look for order in meaningful events
3. confirmation bias: atted to what we agee w/, ad igoe what we do’t
Hallmarks of Scientific Method
Cal “aga: “iee is a uiue
i of opeess ad skeptiis.
Rihad Fea: “iee is a
wa of tig ot to fool ouself.
You ust dout epets.
1. objective observation and logically necessary conclusions
2. parsimonious explanations – stick w/ efficient explanation
3. independent replication
4. skepticism
5. careful designs
6. falsifiability – predictions that can potentially be falsified
7. open mind – to ideas of others
Research
Process
(a cycle)
1. theory e.g. low self-esteem feeds depression
2. hypothesis e.g. people w/ low self-esteem score higher on depression scale
3. research/observations e.g. administer tests, look for correlation
4. generate or refine the theory
Results
- inaccurate beliefs discredited, replaced w/ accurate beliefs
- no tech to study/progress, so we remain temporarily ignorant
Vocab
1. theory: explanation integrates principles, organizes & predicts behaviour or events
2. hypothesis: testable prediction to endable us to keep, reject, or revise the theory
3. construct: what is being measured
4. operational definition: method used to define/measure variable in study
5. replication: same finding w/ different participants in different situations
6. random sampling: each member of population has = chance of inclusion in sample
(also called unbiased sample)
3 Basic Types of
Scientific Studies
1. descriptive
purpose: careful and accurate description
methods:
- questionnaires & interviews
- naturalistic observation
- case studies
e.g.: amount of sleep and depression
2. correlational
purpose: evaluating relationships
methods:
- correlation coefficient: number between -1 to +1,
shows direction & strength of relationship
- scatterplot: graph comprised of points generated
by values of 2 variables (slope=direction,
scatter=strength)
common error:
- illusory correlation: perception of relationship
where none exists (believe one large outcome)
e.g.: is amount of sleep related to depression
3. experimental
purpose: explore cause and effect
methods:
- focus on effect of independent variable
(manipulated) on dependent variable
(potentially influenced)
- observe experimental condition (exposed to
treatment) vs control condition (unexposed)
- placebo: inert substance/condition replacing
active treatment agent
- random assignment: assigned using chance
alone minimize differences across groups
- double-blind: participants & researchers
uawae of eah patiipat’s status
Statistics:
analyze &
interpret data
using objective
measures (NOT
human judgment),
conveniently
1. descriptive
statistics:
describe data
accurately
and
meaningfully
measures of central tendency:
- mode: most frequently occurring
- median: middle score (rank-ordered distribution)
- mean: arithmetic average (add, divide # scores)
measures of variation:
- range: difference between highest & lowest
- standard variation: avg variation around mean
distribution:
- normal distribution: score frequency decreases as
scores get farther from most frequent
- skewed distribution: extreme values pull the
median and mode
2. inferential
statistics:
statistical statement of
how frequently a result
could occur by chance
reliable when
- large samples
- large mean difference (correlational
relationship)
- less variability
significance
- correlation is unlikely to happen by chance
- p<0.5