thinking critically with psychological science · thinking critically with psychological science ....

Chapter One

Thinking Critically With

Psychological Science

Intuition An effortless, immediate,

unreasoned sense of truth

David Myers- author of our text

said “Instinct has the power to hush

reason. But when is it safe to go

with your gut? Researchers may

remain uncertain about the

reliability of intuition, but it is a

difficult force to deny.”

Pitfalls of in thinking that make

intuition and common sense

untrustworthy:

Hindsight bias and overconfidence

Hindsight Bias: the tendency to believe, after learning an outcome, that one would have foreseen it.

Have you ever watched Jeopardy with someone, and after the answer is given that person says "I knew that one" or "That was an easy one"?

Take Ken Jennings…he won 2.5 Million on Jeopardy…but he did have this blooper…

Overconfidence is the tendency to think

we know more about an issue than we

actually do and to overestimate the

accuracy of that knowledge.

For example, for certain types of questions,

answers that people rate as "99% certain"

turn out to be wrong 40% of the time

Scientific Attitude Is being willing to accept only carefully and objectively verified

facts, and to hold a single fact above the authority of the oldest theories. Nothing can be called

scientific that is not based on such an attitude.

The guide to a scientific attitude is Curiosity, Skepticism, and Humility.

Critical Thinking

Thinking that does not blindly

accept arguments and conclusions.

Rather it assumptions, discerns

hidden values, evaluates evidence,

and assesses conclusions.

Such examples given by the text:

Massive losses of brain tissue early in

life may have minimal long term

effects.

Scientific Method

Theory- explains a set of principles that organizes observations and predicts behaviors or events.

Hypotheses- Educated Guess- the testable predictions often implied by a theory.

Bias-Particular belief that restrains openness of the scientific method outcomes

Operational definition- statement of the procedures used to define research variables. (What is the measure of say…anger? Or what is the independent variable or dependent variable)

Scientific Method

Replication-repeating the essence of a

research study, usually with different

participants in different situations, to see

whether the basic finding extends to other

participants and circumstances.

What is the importance of

theories in Psychology?

Because they are trying to Explain,

Organize, and Predict Behavior of the

events under the study/research!

The Good Theory has two

elements:

1. Organizing and linking

observed facts

2. Implying hypotheses that offer

testable predictions and

sometimes practical

applications.

Research Goals

Descriptive Approach to Research-

observe, measure, describe.

An example:

An opinion poll to determine which Presidential candidate people plan to vote for in the next election.

Descriptive studies do not seek to measure the effect of a variable; they seek only to describe.

Research Goals

Correlational Approach to Research-

two different types of behavior and evaluate the relationship between them.

Example:

a study that looked at the proportion of males and females that would purchase either a classical CD or a jazz CD would be studying the relationship between gender and music preference.

Research Goals

Experimental Approach to Research- investigate cause and effect relationships by manipulating one aspect of the aspect that is thought to produce a change in that particular behavior.

When using experimental research scientists usually, but not always, conducted in a laboratory. The laboratory environment allows the experimenter to make controlled observations using the steps of the scientific method.

Example of Experimental Research 1. Theory or research Question- The study is about

whether certain environmental conditions improve or adversely affect motor performance.

2. The investigator might give operational definitions to the environmental condition of interest as “background music” and the motor performance as “typing speed.”

3. Next, the investigator proposes an answer to the research question (“What is the relationship between typing speed and background noise?), an answer called a hypothesis.

4.Define Variables: the relationship between two variables, an independent variable (that which the experimenter manipulates—in this case, the background music) and a dependent variable (that which changes as a consequence of manipulation of the independent variable—in this case, the typing speed). The experimenter hypothesizes that “an increase in loudness of background music will produce a decrease in typing speed.”

subjects would be taken to a laboratory for

testing and would use the same typewriters

to take the typing tests.

The experimenter would have to decide

whether to use two groups of subjects with

comparable typing skills and expose one

group to a music loudness level different

from that used with the other or

sequentially expose the same subjects to

music of two loudness levels.

Each procedure has advantages and

disadvantages.

Descriptive Studies/Research

Methods of study that try to

DESCRIBE a population

Case Study

Definition: an observation technique in

which one person is studied in depth in

the hope of revealing universal principles

Examples:

Genie the Wild Child

Phineas Gage

Anorexia Studies

http://www.youtube.com/watch?v=h7lEocPcyuA

Case Study

Advantages and Disadvantages

What’s good about

case studies?

One person can tell

us a lot about humans

in general

What’s bad about case studies?

One case may be misleading!!!

“My uncle smoked two packs a day for sixty years and never had health problems!”

Survey

Definition: asks people to report their

behavior or opinions

Examples:

Political polling

U.S. Census

Thomas Co. Youth

Survey

Survey


What’s good

about surveys?

Cheap to

administer

Gather a lot of

information about

a lot of people

quickly

What’s bad about

surveys?

Wording Effects

“affirmative action”

vs. “preferential

treatment”; “welfare”

vs. “aid to the needy”

Sampling Error

People Lie

Survey

Representative Sample-

Researcher attempts to select

individuals which are

representative of a larger

population.

Truly representative sampling is

very hard to accomplish and

researchers may dedicate a great

deal of time and funding to get

the most representative sample

as possible.

Survey

Random

Sample- fairly

represents a

population

because each

member has an

equal chance of

inclusion.

Experimentation- a research method

in which an investigator manipulates on or

more factors to observe the effect on some

behavior or mental process

Randomly assigning-

assigning participants

to experimental and

control groups by

chance, thus

minimizing preexisting

differences between

those assigned to

different groups

Grouping

Experimental- group that

is exposed to the treatment

(to one version of the

independent variable)

Control- group that is not

exposed to the treatment.

Serves as a comparison for

evaluating the effect of the

treatment.

Placebo-(placebo effect) results

caused by expectations alone

(belief in it). Substance or

condition which the recipient

assumes is an active agent.

Double Blind Procedure- both

researcher and participants are

ignorant about whether the

research participants have

received the treatment

or a placebo.

Naturalistic Observation

Definition:observing and recording

behavior in NATURALLY occurring situations

We do NOT interfere in naturalistic observations…we simply watch and record!

Example: Jane Goodall observing chimpanzees

http://www.youtube.com/watch?v=EaEDeRJKN0s

http://www.youtube.com/watch?v=EaEDeRJKN0s

Naturalistic Observation


What’s good about

naturalistic

observation?

See authentic

behavior

What’s bad about

naturalistic

observation?

Can’t interfere at all

Correlations

Correlations Coefficient

How closely two things vary together (how well one predicts the other).

Ex. This is a measure of the direction (positive or negative) and extent (range of a correlation coefficient is from -1 to +1) of the relationship between two sets of scores.

Scores with a positive correlation coefficient go up and down together (as with smoking and cancer).

A negative correlation coefficient indicates that as one score increases, the other score decreases (as in the relationship between self-esteem and depression; as self-esteem increases, the rate of depression decreases).

Scatter Plots

Depiction of the relationship between two

variables by means of a graphed cluster

of dots.

Is the following scatter plot positive or

negatively skewed in correlation?

Positive

Is the following scatter plot positive or

negatively skewed in correlation?

Negative

Guess the Correlation…Positive or

Negative.

Education and years in jail—people who

have more years of education tend to

have fewer years in jail (or phrased as

people with more years in jail tend to

have fewer years of education)

Negative

Correlation


Negative.

Happiness and helpfulness—as people’s

happiness level increases, so does their

helpfulness .

Positive


Negative.

SAT scores and college achievement—

among college students, those with higher

SAT scores also have higher grades

Positive


Negative.

Students with higher grades tend to spend

less time watching TV

Negative


Negative.

Cities with more stores selling

pornography have higher rates of

violence.

Positive


Negative.

Babies who are held less tend to cry more

Negative


Negative.

The longer couples have been together

the more similar they are in their

attitudes and opinions.

Positive


Negative.

A researcher finds that students who

have more absences get poorer grades.

Negative

As the number of absences increase, the grade declines. The variables are changing in the opposite direction.

We might find this relationship for many reasons:

(1) Students who are more absent miss important pieces of information that would increase their chances of performing better in the class.

(2) Students who have difficulty in a class may stop attending because they see no reason for going. Thus, does the greater class absence "cause" the poor grades or do poor grades "cause" the greater absence.

(3) A student who is not highly motivated may be absent more often and may do poorly. Thus, these two variables are related to other variables (such as motivation) which may be the real reason for the relationship between class absences and grades.

One big thing to know about

Correlations…

correlations does

not equal

causation

Copyright © Allyn & Bacon 2007

How Do We Make Sense

of the Data?

Researchers use statistics for

two major purposes:

(1) descriptively to characterize

measurements made on groups

or individuals and

(2) inferentially to judge whether

these measurements are the

result of chance


Organizing the Data

First results must be arranged in a

summary chart known as a frequency

distribution

We can convert the data into a bar graph

called a histogram


Frequency distribution

chart – shows how

frequently each score

occurred.

Histogram or bar chart – gives a

visual representation of how the

scores look. This helps us to “see”

whether or not the scores are evenly

distributed.

A histogram can

also show whether

or not the scores

are more clustered

around the middle

of the distribution

or if there are

outliers (extreme

scores).

Examples of

organizing the

data:

Descriptive Statistics

• In order to understand the data that was gathered,

statistics help to bring the data into sharper focus.

• When using statistics, researchers are looking for

the central point around which the numbers seem to

cluster. This is called “measures of central

tendency.”

• This will then help the researchers to make

inferences about the data to determine if the results

are reliable or simply due to chance (e.g. inferential

statistics).



Describing the Data With

Descriptive Statistics Descriptive statistics: Numbers that describe the

main characteristics of the data.

• The mean

• The median

• The mode

• The range

• The standard deviation

• The normal distribution

The Mean • The measure of central tendency most often used to describe a set of

data.

• Add all the scores and divide by the number of scores.

• It is the average.

• While it is a pretty good indicator of the center of the distribution, its

one flaw is that it can be skewed by extreme scores.

• So, if the distribution of the scores is relatively symmetrical (bell

shaped), there is no problem; however, if more scores fall toward

either end of the distribution, then the mean gets pulled in that

direction and distorts the overall inference of the data.


Examples of Skewed Distributions


On the last test, the class mean was 68. But, because it was not a symmetrical

distribution, that sounds like the class overall did poorly. When calculating the

median the scores look much better: the median score was 72. Due to low

extreme scores, the mean is a not a very good indicator of how the class did.

More low scores than high scores – but there are a few extremely

high scores (mean is higher than the median)

More high scores than low – but there are a few extremely low scores (mean

is lower than the median)

The Median • The “middle” score.

• Think of the “median divider” in the center of the road – it

divides the upper half of the scores from the lower half.

• This is a better measure of central tendency because it is not

affected by extreme scores.

• The scores are listed in order, and it is the number in the

middle. (e.g., 50, 55, 60, 65, 70)

• If you have an uneven set of numbers, take the two middle

numbers, add them, and divide by 2. (e.g., 50, 55, 60,

65, 70, 72..add 60+65/2=62.5).


The Mode

• A measure of central tendency that is

used to identify the score that occurs the

mode, ooops, the most!

• 55, 55, 55, 63, 68, 70, 70, 82, 95

• It is often the least useful measure of

central tendency, especially if the sample

is small.


The Range

• The simplest measure of central tendency

that represents the difference between the

highest and the lowest values.

• You use the range all the time in school

when you see what differentiates an A

from a B (90-100 and 80-89).


The Standard Deviation • Psychologists prefer to take all scores into consideration,

not just the highest and the lowest, so they use the standard

deviation instead.

• The SD is a measure of central tendency that shows an

average difference between each score and the mean.

• So, we are looking at the changes in the scores across the

spectrum of the scores.

• The larger the SD, the more spread out the scores are; the

smaller the SD, the more the scores bunch together at the

mean.


The Normal Distribution • Together, the SD and the mean tell us much about a

distribution of scores. They indicate where the

center of the distribution is and how closely the

scores cluster around the center.

• In a normal distribution, or a bell curve, the scores

are all equally distributed around the mean.


Normal Distribution


68% of values

% of scores

• 68% of scores fall within 1

SD above and below the

mean

•If you have 100 scores, 50

are above, and 50 are below

•Know how to compute

percentile

•Know how to compute Z

score

2% 14% 34% 34% 14% 2%

95% of values

99% of values

Mean

Percentiles

0

Standard

Deviations from

the Mean

Z Scores

-1 -2 -3 -4 +1 +2 +3 +4

0 2nd 16th 50th

+1 0 -1

-2 -3

-4

84th 98th

100th

+2 +3

+4


Making Inferences with Inferential

Statistics • Inferential statistics are used to assess whether the results of a study are reliable

or whether they might be simply the result of chance.

• Researchers use inferential statistics to determine whether or not the findings can

be applied to the larger population from which the sample was selected.

• Researchers compare the results of the experimental group to the control group

and determine (infer) whether the differences between the groups are a result of

the Independent Variable or could be the result of chance.

• To have confidence in the results, the researchers have to take into account the

magnitude of the differences in scores, and go back to make sure the sample was

large enough and that the sample was representative of the population at large.

While a sample can never truly represent the entire population, researchers do

look at sampling error, or how chance plays a factor in the results.

Making Inferences with Inferential

Statistics • Researchers then compute a “p” value for the scores, which states

how probable the results are due to the IV or chance.

• What you need to know is that, in psychology, the cutoff for

statistical significance, or that the results are probably due to the IV,

is a value of p<.05. This means that the probability of the results of

the experiment being due to chance are less than 5%, or 5 in 100.

• A “p” value can never equal zero because we can never be 100%

sure that results did not happen due to chance.

• Therefore, researchers often try to replicate their results to gather

more evidence that their initial findings were not due to chance.


Statistics- Numerical data

Statistics are used to make sense of the

data:

Researchers use statistics for two major

purposes:

1.

Limitations of Descriptive

Studies

These methods can contribute to overall understanding, but do not show causation.

Surveys and observation can show correlation

Correlation enables prediction.

Correlation does not equal causation!

Ice Cream Mystery

Statistical Reasoning in Everyday

Life

Statistics organize, summarize, and make inferences from data.

Three measures of Central Tendency :

1. Mode- most frequently occurring score in a distribution.

2. Mean is the arithmetic average of a distribution. (add scores then divide by number of scores)

3.Median is the middle score in a distribution; half the scores are above it and half are below it.

Two measures of Variation:

1. Range (of scores)- gap between the lowest and highest score (this shows only a rough estimate of variation)

2. Standard deviation – measures how scores deviate form one another. It better gauges whether scores are packed together or dispersed because it uses information from each score. Many types of scores are distributed alone a bell shaped curve or a normal curve.

Dark blue is less than one standard deviation from the mean. For the normal

distribution, this accounts for about 68% of the set (dark blue), while two

standard deviations from the mean (medium and dark blue) account for about 95%,

and three standard deviations (light, medium, and dark blue) account for

about 99.7%

http://en.wikipedia.org/wiki/File:Standard_deviation_diagram.svg

How do psychologists decide whether

differences are meaningful?

Tests of Statistical significance

determines whether difference between

two groups are reliable. When the

averages (mean) of the samples drawn

from the groups are reliable and the

difference between them is relatively

large, we say the difference has statistical

significance.

Correlation Correlation can be:

causation (but not the other way around),

influenced by 3rd variables

pure chance

Correlation Coefficient

Scatterplots – illustrate correlation

Correlation?

Positive

Correlation

Statistically significant

inverse relationship

between pirates and

global temperature

Illusory Correlation

Psychologists studies with animals

and Humans Psychologists use animals because their

physiological and psychological processes enable them to better understand the similar processes that operate in humans.

Ethical guidelines in animals in experiments: Rarely do they experience pain in psychological experiments.

Ethical treatment of humans urge investigators to obtain informed consent, protect subjects from harm and discomfort, treat information about individuals confidentially and fully explain the research afterward.

thinking critically with psychological science · thinking critically with psychological science ....

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