# 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