1. The document presents 11 claims and then states that all of them are false. It emphasizes the importance of the scientific method to verify answers rather than accepting claims without evidence. 2. People's beliefs and knowledge can come from many sources like rumors, parents, friends, and experiences that are not always accurate. The scientific method helps minimize errors by standardizing steps that can be replicated. 3. The last part of the document discusses identifying problems, forming testable hypotheses, designing studies, collecting and analyzing data, and reporting findings to help verify answers through the scientific process.

2. 1. We need a full brain to function completely
2. Drinking coffee can help sober you up
3. Reading in dim light can ruin eyesight
4. Overweight kids are still carrying “babyfat” that will melt
away as they grow
5. Kids raised by homosexual parents have higher rates of
homosexuality
6. Women are worse drivers than men
7. Brainstorming ideas as groups is more productive
8. Most people who are abused as kids become abusers
9. The age group at highest risk of suicide is adolescents
10. Homicide is more common than suicide
11. Nicotine is less addictive than harder drugs

3.  ALL of the questions from the previous slide
are FALSE!
 This is why we need the Scientific Method – we need
to verify answers that we think we know.
 We might have heard the answers from a parent,
teacher, friend, internet…… they might have been
right, they might have been joking, they might have
been wrong, they might have been lying!

4.  Where do your beliefs, knowledge come from?
 Rumor, class, parents, friends, religion, can’t
remember, your experiences?
 These are NOT always accurate sources.
 Scientific Method = STANDARDIZATION!
 minimizes error, rumor, & made up stuff
 Replication is easier because we are all using the
same steps in the scientific method.

5. Identify Problem
Theory of “WHY/SOLUTION” for a general issue
1. Formulate a testable hypothesis
2. Design study
3. Collect data
4. Analyze data and draw conclusions
5. Report findings
 Replication

6.  Operationally Define Variables
 Choose between research options
 Descriptive, Correlation, Experiment
 Choose data collection options
 S.O.T.L.
 Survey (self-report)
 Observation
 Test Data
 Life Outcome Data

7.  Operational Definition
 Researcher’s description of how the variable will be
used in his/her study.
 Reason we need one: people interpret things different;
this is a problem for researchers. If I ask 100 people
how much they ate yesterday, one might say 3
(meaning 3 meals) and someone else might say 2,000
(meaning calories). We don’t want participants to
have to interpret the variable, so we do it for them.
 What we need to define:
a) Unit of measurement (need to narrow down to 1)
b) What counts (what situations apply/don’t apply here)

8.  Unit of measurement
 Height
 COULD BE: inches, feet, meters, hands (this is how you
measure a horse). In an operational definition you (as
the researcher) get to choose (1) of these. For instance:
Inches.
 TV
 Could be: how many TVs you have, how many hours
you watch, rate quality of yours 1-10 where 1= terrible
and 10=state of the art. You would need to choose
what unit you will be measuring.

9.  What Counts
 As a researcher you also need to identify what counts
and what doesn’t count and under what conditions
 Height: do I measure with my shoes ON or OFF? Do I
measure to the top of the skull or hair (I might have
spikey hair that gives me 2+ inches)? Do I measure all
slumped over or standing with back against a wall? Do I
measure as of today or 5 years ago before I was done
growing?
 TV: do I measure yesterday’s TV viewing or last week’s?
Do I count when the TV was on in the background, but I
was working on my computer? Do I count when the TV
was on but I was napping? Do I count when I was
watching a TV show streaming to my phone or only the
box on the wall?

10.  Descriptive
 Describes existing variable
 Correlational
 Looks at Relationships/Associations between two
items (used for future prediction)
 Experimental
 Looks at causality between items (used for
explanation)

11.  Psychologists describe existing behavior or
characteristics of individuals engaging in it
 Avg age, education level, number of occurrences,
typical environment, etc

12. 0
25
5
30
15
10
20
GirlsBoys
Percentage
parent–child
interactions
in which the
parent
explained
science
concepts
Parents’
Explanations
of Science to
Sons and
Daughters at
a Science
Museum

13.  Examines the relationship between TWO variables
 Variables must be
 Continuous
 naturally occurring
 Relationship description:
 Strength
 Direction
 Used for making future predictions
 Measure TWO variables from EACH participant
 Scatter-plot

14.  Prediction of outcome
 Strength and direction
 Strength
 Mild, moderate, strong
 Direction
 Positive or Negative (or description)

15. 1. What is your shoe size?
2. What is your current GPA?
1. What is your golf ability (scale of 1-10)?
2. What is your golf handicap score?
1. What is your annual income?
2. What is your weight in lbs?

16.  Correlation Coefficient
 a statistical measure relationship between variables
Correlation
coefficient
r = +.37
Indicates direction
of relationship
(positive or negative)
Indicates strength
of relationship
(0.00 to 1.00)
Next

17. Positive Negative
X Y X Y X Y X Y
Back
 Negative Direction
Examples
• Golf ability & Golf
Score
• Minutes workout &
weight
• Class attendance &
failure rate
 Positive Direction
Examples
• Age & Height
• Hours practices &
playing time
• Liquid consumption &
frequency of urination

18.  How well one
variable predicts
the other. (0-1)
 Closer to 1 = better
predictive ability
 Closer to 0 – less
predictive ability
Back
1
2

19. Three Possible Cause-Effect Relationships
(1)
Low self-esteem
Depression
(2)
Depression
Low self-esteem
Low self-esteem
Depression
(3)
Distressing events
or biological
predisposition
could cause
could cause
could cause
or
or
and

20.  We cannot make causal statements as there are
TOO many possible cause and effect options
 If I want to know cause and effect, I have to
perform an Experiment.
 It is set up different – so we are testing (1) possible
cause and effect relationship.

21.  Experiment
 Method in which one variable is MANIPULATED
under CONTROLLED conditions (IV) and observes
changes in a second variable (DV)
 Independent variable (IV) – a condition or event
that is “manipulated” in order to see if it impacts
another variable
 Dependant Variable (DV) – variable that is thought
to be affected by the IV (measured by the
experimenter)

22. IV
Condition
1
Condition
2
 MUST have AT LEAST
2 Levels/conditions of
IV

23. IV
Condition
1
Condition
2
Condition
3
Condition
4
But Could have
as many as you
wanted.

24. IV
Condition 1 Condition 2 Condition 3
Same DV
measurement

25. IV
Light Level
Condition 1
No Light
Condition 2
Low Light
Condition 3
Bright Light
Same Exam
over lecture
material

26.  Prediction of outcome
 Cause and effect
 Which level of IV would do better/worse than
other(s)
 Students taking the test in the No light
condition would receive lower test scores than
those in the low and bright light conditions,
and there would be no difference between
students in the low and bright light conditions.

27. IV
Handedness
DV
“Quality of writing”
How many letters are
“correct” shape
Dominate Hand
Non-Dominate Hand
Compare Those who used Dominate hand to those who
used Non-Dominate hand.

29.  Hypothesis: People who smell
“bad” things will not eat as much as
those who do not smell anything
in particular.
 ½ of you will smell a skunk
 ½ will not smell anything specific
 I am going to keep track of (through observation)
who eats more potato chips in 15 minutes
 What is the IV (and levels), what is the DV?

30.  IV
 Smell in room
 Conditions:
 Skunk
 Nothing in particular
 DV
 how much eaten
 Operational definition: how many individual chips you
eat in the next 15 minutes

31.  Experimental Condition(s)
 the condition(s) of the IV that exposes participants to
the “active treatment” or the “non-status quo”
 Minimum of 1 experimental condition
 Control Condition
 the condition of an experiment that is the “non-active
treatment” or the “status quo”
 serves as a comparison for evaluating the effect of the
treatment
 Max of 1 control condition

32. IV: Light
Condition
Condition1:
Full Light
Control? Exp?
Condition2:
Partial Light
Control? Exp?
Condition3:
No Light
Control? Exp?

33. IV: Light
Condition
Condition1:
Full Light
Control
Condition2:
Partial Light
Exp
Condition3:
No Light
Exp
Since you “normally” take exams in full light, that is the normal or
control group. The others are not typical, and therefore
Experimental Groups

34.  Random Assignment
 assigning participants levels/conditions of the IV
 Assign to levels by chance
 each participant has the exact same likelihood of being
placed in ANY of the conditions
 minimizes pre-existing differences between those
assigned to the different groups
 Works based in Statistical probabilities

35.  Placebo Effect
 Occurs when changes in behavior are produced by a
cognitive “decision” rather than the IV itself.
 To solve:
 A “fake” substance or condition administered instead of
an “active” agent, to see if it triggers the effects believed
to characterize the active agent
 Double-Blind Procedure
 both the research participants and the research staff are
“blind” to whether the research participants have
received the treatment or a placebo
 commonly used in drug-evaluation studies

36. Advantages
 Causality
conclusions
 Strength of
generalization and
control
Limitations
 Artificial
 Lack of complexity
 Cannot be used for
everything

38.  Surveys - Participants are asked direct questions about a given
topic
 Example: how intelligent are you on a 1-10 scale where 1=not
at all and 10=most ever
 Observation – participants are watched to identify their level
on a topic
 Naturalistic – in a natural setting
 Laboratory – in a laboratory setting
 Test Data – Participants are asked indirect questions about a
given topic
 Example: given an IQ test to interpret how intelligent you are
 Life Outcome Data – using existing data sources to pull
what we need
 Example: use your existing ACT score to use as Intelligence
measurement rather than taking a new one

39.  Population
 all the cases in a group, from which samples may
be drawn for a study
 Random Sample
 a sample that fairly represents a population
because each member has an equal chance of
inclusion
 Convenience Sample
 A sample of the population. Volunteers who
were available to the researcher.

40.  Descriptive statistics- summarized data for large
groups of participants.
 Measures of Central Tendency-
 A number describing a “typical score,” around which
others fall
 Mean- average
 Add all scores divide by N
 Median- midpoint in rank-ordered data
 ½ scores above, ½ below
 Mode- score appearing most often