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Browse Content / Thinking like a Psychological Scientist

Thinking like a
Psychological
Scientist
By Erin I. Smith
California Baptist University

We are bombarded every day with claims about

how the world works, claims that have a direct
impact on how we think about and solve problems
in society and our personal lives. This module
explores important considerations for evaluating
the trustworthiness of such claims by contrasting
between scientific thinking and everyday
observations (also known as “anecdotal
evidence”).

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Tags:
Facts, Falsifiable, Hypothesis, Inductive reasoning,
Levels of analysis, Null hypothesis significance testing,
Theory, Type I error, Type II error, Values

Learning Objectives

Compare and contrast conclusions based on

scientific and everyday inductive reasoning.

Understand why scientific conclusions and

theories are trustworthy, even if they are not
able to be proven.

Articulate what it means to think like a

psychological scientist, considering qualities of
good scientific explanations and theories.

Discuss science as a social activity, comparing

and contrasting facts and values.

Introduction

Why are some people so much happier than

others? Is it harmful for children to have
imaginary companions? How might students
study more effectively?

Today, people are overwhelmed with information

although it varies in quality. [Image: Mark Smiciklas,
https://goo.gl/TnZCoH, CC BY-NC 2.0,
https://goo.gl/AGYuo9]

Even if you’ve never considered these questions

before, you probably have some guesses about
their answers. Maybe you think getting rich or
falling in love leads to happiness. Perhaps you
view imaginary friends as expressions of a
dangerous lack of realism. What’s more, if you
were to ask your friends, they would probably also
have opinions about these questions—opinions
that may even differ from your own.

A quick internet search would yield even more

answers. We live in the “Information Age,” with
people having access to more explanations and
answers than at any other time in history. But,
although the quantity of information is continually
increasing, it’s always good practice to consider
the quality of what you read or watch: Not all
information is equally trustworthy. The
trustworthiness of information is especially
important in an era when “fake news,” urban
myths, misleading “click-bait,” and conspiracy
theories compete for our attention alongside well-
informed conclusions grounded in evidence.
Determining what information is well-informed is
a crucial concern and a central task of science.
Science is a way of using observable data to help
explain and understand the world around us in a
trustworthy way.

In this module, you will learn about scientific

thinking. You will come to understand how
scientific research informs our knowledge and
helps us create theories. You will also come to
appreciate how scientific reasoning is different
from the types of reasoning people often use to
form personal opinions.

Scientific Versus Everyday

Reasoning

Each day, people offer statements as if they are

facts, such as, “It looks like rain today,” or, “Dogs
are very loyal.” These conclusions represent
hypotheses about the world: best guesses as to
how the world works. Scientists also draw
conclusions, claiming things like, “There is an 80%
chance of rain today,” or, “Dogs tend to protect
their human companions.” You’ll notice that the
two examples of scientific claims use less certain
language and are more likely to be associated with
probabilities. Understanding the similarities and
differences between scientific and everyday (non-
scientific) statements is essential to our ability to
accurately evaluate the trustworthiness of various
claims.

Scientific and everyday reasoning both employ

induction: drawing general conclusions from
specific observations. For example, a person’s
opinion that cramming for a test increases
performance may be based on her memory of
passing an exam after pulling an all-night study
session. Similarly, a researcher’s conclusion
against cramming might be based on studies
comparing the test performances of people who
studied the material in different ways (e.g.,
cramming versus study sessions spaced out over
time). In these scenarios, both scientific and
everyday conclusions are drawn from a limited
sample of potential observations.

The process of induction, alone, does not seem

suitable enough to provide trustworthy
information—given the contradictory results.
What should a student who wants to perform well
on exams do? One source of information
encourages her to cram, while another suggests
that spacing out her studying time is the best
strategy. To make the best decision with the
information at hand, we need to appreciate the
differences between personal opinions and
scientific statements, which requires an
understanding of science and the nature of
scientific reasoning.

There are generally agreed-upon features that

distinguish scientific thinking—and the theories
and data generated by it—from everyday thinking.
A short list of some of the commonly cited
features of scientific theories and data is shown in
Table 1.

Table 1. Features of good scientific theories (Kuhn, 2011)

One additional feature of modern science not

included in this list but prevalent in scientists’
thinking and theorizing is falsifiability, a feature
that has so permeated scientific practice that it
warrants additional clarification. In the early 20th
century, Karl Popper (1902-1994) suggested that
science can be distinguished from pseudoscience
(or just everyday reasoning) because scientific
claims are capable of being falsified. That is, a
claim can be conceivably demonstrated to be
untrue. For example, a person might claim that
“all people are right handed.” This claim can be
tested and—ultimately—thrown out because it
can be shown to be false: There are people who
are left-handed. An easy rule of thumb is to not
get confused by the term “falsifiable” but to
understand that—more or less—it means
testable.

On the other hand, some claims cannot be tested

and falsified. Imagine, for instance, that a magician
claims that he can teach people to move objects
with their minds. The trick, he explains, is to truly
believe in one’s ability for it to work. When his
students fail to budge chairs with their minds, the
magician scolds, “Obviously, you don’t truly
believe.” The magician’s claim does not qualify as
falsifiable because there is no way to disprove it. It
is unscientific.

Popper was particularly irritated about

nonscientific claims because he believed they
were a threat to the science of psychology.
Specifically, he was dissatisfied with Freud’s
explanations for mental illness. Freud believed
that when a person suffers a mental illness it is
often due to problems stemming from childhood.
For instance, imagine a person who grows up to
be an obsessive perfectionist. If she were raised
by messy, relaxed parents, Freud might argue that
her adult perfectionism is a reaction to her early
family experiences—an effort to maintain order
and routine instead of chaos. Alternatively,
imagine the same person being raised by harsh,
orderly parents. In this case, Freud might argue
that her adult tidiness is simply her internalizing
her parents’ way of being. As you can see,
according to Freud’s rationale, both opposing
scenarios are possible; no matter what the
disorder, Freud’s theory could explain its
childhood origin—thus failing to meet the
principle of falsifiability.

Karl Popper was an influential thinker regarding

scientific theory and reasoning. [Image: Lucinda
Douglas-Menzies, https://goo.gl/uuqxCe]

Popper argued against statements that could not

be falsified. He claimed that they blocked scientific
progress: There was no way to advance, refine, or
refute knowledge based on such claims. Popper’s
solution was a powerful one: If science showed all
the possibilities that were not true, we would be left
only with what is true. That is, we need to be able to
articulate—beforehand—the kinds of evidence
that will disprove our hypothesis and cause us to
abandon it.

This may seem counterintuitive. For example, if a

scientist wanted to establish a comprehensive
understanding of why car accidents happen, she
would systematically test all potential causes:
alcohol consumption, speeding, using a cell
phone, fiddling with the radio, wearing sandals,
eating, chatting with a passenger, etc. A complete
understanding could only be achieved once all
possible explanations were explored and either
falsified or not. After all the testing was concluded,
the evidence would be evaluated against the
criteria for falsification, and only the real causes of
accidents would remain. The scientist could
dismiss certain claims (e.g., sandals lead to car
accidents) and keep only those supported by
research (e.g., using a mobile phone while driving
increases risk). It might seem absurd that a
scientist would need to investigate so many
alternative explanations, but it is exactly how we
rule out bad claims. Of course, many explanations
are complicated and involve multiple causes—as
with car accidents, as well as psychological
phenomena.

Test Yourself 1: Can It Be

Falsified?
Which of the following hypotheses can be
falsified? For each, be sure to consider what kind
of data could be collected to demonstrate that a
statement is not true.

A. Chocolate tastes better than pasta.

B. We live in the most violent time in history.

C. Time can run backward as well as forward.

D. There are no planets other than Earth that

have water on them.

[See answer at end of this module]

Although the idea of falsification remains central

to scientific data and theory development, these
days it’s not used strictly the way Popper originally
envisioned it. To begin with, scientists aren’t solely
interested in demonstrating what isn’t. Scientists
are also interested in providing descriptions and
explanations for the way things are. We want to
describe different causes and the various
conditions under which they occur. We want to
discover when young children start speaking in
complete sentences, for example, or whether
people are happier on the weekend, or how
exercise impacts depression. These explorations
require us to draw conclusions from limited
samples of data. In some cases, these data seem
to fit with our hypotheses and in others they do
not. This is where interpretation and probability
come in.

The Interpretation of Research

Results

Imagine a researcher wanting to examine the

hypothesis—a specific prediction based on
previous research or scientific theory—that
caffeine enhances memory. She knows there are
several published studies that suggest this might
be the case, and she wants to further explore the
possibility. She designs an experiment to test this
hypothesis. She randomly assigns some
participants a cup of fully caffeinated tea and
some a cup of herbal tea. All the participants are
instructed to drink up, study a list of words, then
complete a memory test. There are three possible
outcomes of this proposed study:

1. The caffeine group performs better (support

for the hypothesis).

2. The no-caffeine group performs better

(evidence against the hypothesis).

3. There is no difference in the performance

between the two groups (also evidence against
the hypothesis).

Let’s look, from a scientific point of view, at how

the researcher should interpret each of these
three possibilities.

First, if the results of the memory test reveal that

the caffeine group performs better, this is a piece
of evidence in favor of the hypothesis: It appears,
at least in this case, that caffeine is associated
with better memory. It does not, however, prove
that caffeine is associated with better memory.
There are still many questions left unanswered.
How long does the memory boost last? Does
caffeine work the same way with people of all
ages? Is there a difference in memory
performance between people who drink caffeine
regularly and those who never drink it? Could the
results be a freak occurrence? Because of these
uncertainties, we do not say that a study—
especially a single study—proves a hypothesis.
Instead, we say the results of the study offer
evidence in support of the hypothesis. Even if we
tested this across 10 thousand or 100 thousand
people we still could not use the word “proven” to
describe this phenomenon. This is because
inductive reasoning is based on probabilities.
Probabilities are always a matter of degree; they
may be extremely likely or unlikely. Science is
better at shedding light on the likelihood—or
probability—of something than at proving it. In
this way, data is still highly useful even if it doesn’t
fit Popper’s absolute standards.

The science of meteorology helps illustrate this

point. You might look at your local weather
forecast and see a high likelihood of rain. This is
because the meteorologist has used inductive
reasoning to create her forecast. She has taken
current observations—lots of dense clouds
coming toward your city—and compared them to
historical weather patterns associated with rain,
making a reasonable prediction of a high
probability of rain. The meteorologist has not
proven it will rain, however, by pointing out the
oncoming clouds.

Proof is more associated with deductive

reasoning. Deductive reasoning starts with
general principles that are applied to specific
instances (the reverse of inductive reasoning).
When the general principles, or premises, are true,
and the structure of the argument is valid, the
conclusion is, by definition, proven; it must be so. A
deductive truth must apply in all relevant
circumstances. For example, all living cells contain
DNA. From this, you can reason—deductively—
that any specific living cell (of an elephant, or a
person, or a snake) will therefore contain DNA.
Given the complexity of psychological
phenomena, which involve many contributing
factors, it is nearly impossible to make these types
of broad statements with certainty.

Test Yourself 2: Inductive or

Deductive?
A. The stove was on and the water in the pot was
boiling over. The front door was standing open.
These clues suggest the homeowner left
unexpectedly and in a hurry.

B. Gravity is associated with mass. Because the

moon has a smaller mass than the Earth, it
should have weaker gravity.

C. Students don’t like to pay for high priced

textbooks. It is likely that many students in the
class will opt not to purchase a book.

D. To earn a college degree, students need 100

credits. Janine has 85 credits, so she cannot
graduate.

[See answer at end of this module]

The second possible result from the caffeine-

memory study is that the group who had
no caffeine demonstrates better memory. This
result is the opposite of what the researcher
expects to find (her hypothesis). Here, the
researcher must admit the evidence does not
support her hypothesis. She must be careful,
however, not to extend that interpretation to
other claims. For example, finding increased
memory in the no-caffeine group would not be
evidence that caffeine harms memory. Again,
there are too many unknowns. Is this finding a
freak occurrence, perhaps based on an unusual
sample? Is there a problem with the design of the
study? The researcher doesn’t know. She simply
knows that she was not able to observe support
for her hypothesis.

There is at least one additional consideration: The

researcher originally developed her caffeine-
benefits-memory hypothesis based on
conclusions drawn from previous research. That
is, previous studies found results that suggested
caffeine boosts memory. The researcher’s single
study should not outweigh the conclusions of
many studies. Perhaps the earlier research
employed participants of different ages or who
had different baseline levels of caffeine intake.
This new study simply becomes a piece of fabric in
the overall quilt of studies of the caffeine-memory
relationship. It does not, on its own, definitively
falsify the hypothesis.

Finally, it’s possible that the results show no

difference in memory between the two groups.
How should the researcher interpret this? How
would you? In this case, the researcher once again
has to admit that she has not found support for
her hypothesis.

Interpreting the results of a study—regardless of

outcome—rests on the quality of the observations
from which those results are drawn. If you learn,
say, that each group in a study included only four
participants, or that they were all over 90 years
old, you might have concerns. Specifically, you
should be concerned that the observations, even
if accurate, aren’t representative of the general
population. This is one of the defining differences
between conclusions drawn from personal
anecdotes and those drawn from scientific
observations. Anecdotal evidence—derived from
personal experience and unsystematic
observations (e.g., “common sense,”)—is limited
by the quality and representativeness of
observations, and by memory shortcomings. Well-
designed research, on the other hand, relies on
observations that are systematically recorded, of
high quality, and representative of the population
it claims to describe.

Why Should I Trust Science If It

Can’t Prove Anything?

It’s worth delving a bit deeper into why we ought

to trust the scientific inductive process, even
when it relies on limited samples that don’t offer
absolute “proof.” To do this, let’s examine a
widespread practice in psychological science: null-
hypothesis significance testing.

Is there a relationship between student age and

academic performance? How could we research this
question? How confident can we be that our
observations reflect reality? [Image: Jeremy Wilburn,
https://goo.gl/i9MoJb, CC BY-NC-ND 2.0,
https://goo.gl/SjTsDg]

To understand this concept, let’s begin with

another research example. Imagine, for instance,
a researcher is curious about the ways maturity
affects academic performance. She might have a
hypothesis that mature students are more likely
to be responsible about studying and completing
homework and, therefore, will do better in their
courses. To test this hypothesis, the researcher
needs a measure of maturity and a measure of
course performance. She might calculate the
correlation—or relationship—between student
age (her measure of maturity) and points earned
in a course (her measure of academic
performance). Ultimately, the researcher is
interested in the likelihood—or probability—that
these two variables closely relate to one another.
Null-hypothesis significance testing (NHST)
assesses the probability that the collected data
(the observations) would be the same if there
were no relationship between the variables in the
study. Using our example, the NHST would test
the probability that the researcher would find a
link between age and class performance if there
were, in reality, no such link.

Now, here’s where it gets a little complicated.

NHST involves a null hypothesis, a statement that
two variables are not related (in this case, that
student maturity and academic performance are
not related in any meaningful way). NHST also
involves an alternative hypothesis, a statement that
two variables are related (in this case, that student
maturity and academic performance go together).
To evaluate these two hypotheses, the researcher
collects data. The researcher then compares what
she expects to find (probability) with what she
actually finds (the collected data) to determine
whether she can falsify, or reject, the null
hypothesis in favor of the alternative hypothesis.

How does she do this? By looking at the

distribution of the data. The distribution is the
spread of values—in our example, the numeric
values of students’ scores in the course. The
researcher will test her hypothesis by comparing
the observed distribution of grades earned by
older students to those earned by younger
students, recognizing that some distributions are
more or less likely. Your intuition tells you, for
example, that the chances of every single person
in the course getting a perfect score are lower
than their scores being distributed across all
levels of performance.

The researcher can use a probability table to

assess the likelihood of any distribution she finds
in her class. These tables reflect the work, over the
past 200 years, of mathematicians and scientists
from a variety of fields. You can see, in Table 2a,
an example of an expected distribution if the
grades were normally distributed (most are
average, and relatively few are amazing or
terrible). In Table 2b, you can see possible results
of this imaginary study, and can clearly see how
they differ from the expected distribution.

In the process of testing these hypotheses, there

are four possible outcomes. These are
determined by two factors: 1) reality, and 2) what
the researcher finds (see Table 3). The best
possible outcome is accurate detection. This means
that the researcher’s conclusion mirrors reality. In
our example, let’s pretend the more mature
students do perform slightly better. If this is what
the researcher finds in her data, her analysis
qualifies as an accurate detection of reality.
Another form of accurate detection is when a
researcher finds no evidence for a phenomenon,
but that phenomenon doesn’t actually exist
anyway! Using this same example, let’s now
pretend that maturity has nothing to do with
academic performance. Perhaps academic
performance is instead related to intelligence or
study habits. If the researcher finds no evidence
for a link between maturity and grades and none
actually exists, she will have also achieved
accurate detection.

Table 2a (Above): Expected grades if there were no

difference between the two groups. Table 2b (Below):
Course grades by age

There are a couple of ways that research

conclusions might be wrong. One is referred to as
a type I error—when the researcher concludes
there is a relationship between two variables but,
in reality, there is not. Back to our example: Let’s
now pretend there’s no relationship between
maturity and grades, but the researcher still finds
one. Why does this happen? It may be that her
sample, by chance, includes older students who
also have better study habits and perform better:
The researcher has “found” a relationship (the
data appearing to show age as significantly
correlated with academic performance), but the
truth is that the apparent relationship is purely
coincidental—the result of these specific older
students in this particular sample having better-
than-average study habits (the real cause of the
relationship). They may have always had superior
study habits, even when they were young.

Another possible outcome of NHST is a type II

error, when the data fail to show a relationship
between variables that actually exists. In our
example, this time pretend that maturity is —in
reality—associated with academic performance,
but the researcher doesn’t find it in her sample.
Perhaps it was just her bad luck that her older
students are just having an off day, suffering from
test anxiety, or were uncharacteristically careless
with their homework: The peculiarities of her
particular sample, by chance, prevent the
researcher from identifying the real relationship
between maturity and academic performance.

These types of errors might worry you, that there

is just no way to tell if data are any good or not.
Researchers share your concerns, and address
them by using probability values (p-values) to set
a threshold for type I or type II errors. When
researchers write that a particular finding is
“significant at a p < .05 level,” they’re saying that if
the same study were repeated 100 times, we
should expect this result to occur—by chance—
fewer than five times. That is, in this case, a Type I
error is unlikely. Scholars sometimes argue over
the exact threshold that should be used for
probability. The most common in psychological
science are .05 (5% chance), .01 (1% chance), and
.001 (1/10th of 1% chance). Remember,
psychological science doesn’t rely on definitive
proof; it’s about the probability of seeing a specific
result. This is also why it’s so important that
scientific findings be replicated in additional
studies.

Table 3: Accurate detection and errors in research

It’s because of such methodologies that science is

generally trustworthy. Not all claims and
explanations are equal; some conclusions are
better bets, so to speak. Scientific claims are more
likely to be correct and predict real outcomes
than “common sense” opinions and personal
anecdotes. This is because researchers consider
how to best prepare and measure their subjects,
systematically collect data from large and—ideally
—representative samples, and test their findings
against probability.

Scientific Theories

The knowledge generated from research is

organized according to scientific theories. A
scientific theory is a comprehensive framework
for making sense of evidence regarding a
particular phenomenon. When scientists talk
about a theory, they mean something different
from how the term is used in everyday
conversation. In common usage, a theory is an
educated guess—as in, “I have a theory about
which team will make the playoffs,” or, “I have a
theory about why my sister is always running late
for appointments.” Both of these beliefs are liable
to be heavily influenced by many untrustworthy
factors, such as personal opinions and memory
biases. A scientific theory, however, enjoys
support from many research studies, collectively
providing evidence, including, but not limited to,
that which has falsified competing explanations. A
key component of good theories is that they
describe, explain, and predict in a way that can be
empirically tested and potentially falsified.

Early theories placed the Earth at the center of the solar

system. We now know that the Earth revolves around
the sun. [Image: Pearson Scott Foresman,
https://goo.gl/W3izMR, Public Domain]

Theories are open to revision if new evidence

comes to light that compels reexamination of the
accumulated, relevant data. In ancient times, for
instance, people thought the Sun traveled around
the Earth. This seemed to make sense and fit with
many observations. In the 16th century, however,
astronomers began systematically charting visible
objects in the sky, and, over a 50-year period, with
repeated testing, critique, and refinement, they
provided evidence for a revised theory: The Earth
and other cosmic objects revolve around the Sun.
In science, we believe what the best and most data
tell us. If better data come along, we must be
willing to change our views in accordance with the
new evidence.

Is Science Objective?

Thomas Kuhn (2012), a historian of science,

argued that science, as an activity conducted by
humans, is a social activity. As such, it is—
according to Kuhn—subject to the same
psychological influences of all human activities.
Specifically, Kuhn suggested that there is no such
thing as objective theory or data; all of science is
informed by values. Scientists cannot help but let
personal/cultural values, experiences, and
opinions influence the types of questions they ask
and how they make sense of what they find in
their research. Kuhn’s argument highlights a
distinction between facts (information about the
world), and values (beliefs about the way the
world is or ought to be). This distinction is an
important one, even if it is not always clear.

To illustrate the relationship between facts and

values, consider the problem of global warming. A
vast accumulation of evidence (facts)
substantiates the adverse impact that human
activity has on the levels of greenhouse gases in
Earth’s atmosphere leading to changing weather
patterns. There is also a set of beliefs (values),
shared by many people, that influences their
choices and behaviors in an attempt to address
that impact (e.g., purchasing electric vehicles,
recycling, bicycle commuting). Our values—in this
case, that Earth as we know it is in danger and
should be protected—influence how we engage
with facts. People (including scientists) who
strongly endorse this value, for example, might be
more attentive to research on renewable energy.

The primary point of this illustration is that

(contrary to the image of scientists as outside
observers to the facts, gathering them neutrally
and without bias from the natural world) all
science—especially social sciences like psychology
—involves values and interpretation. As a result,
science functions best when people with diverse
values and backgrounds work collectively to
understand complex natural phenomena.

Indeed, science can benefit from multiple

perspectives. One approach to achieving this is
through levels of analysis. Levels of analysis is the
idea that a single phenomenon may be explained
at different levels simultaneously. Remember the
question concerning cramming for a test versus
studying over time? It can be answered at a
number of different levels of analysis. At a low
level, we might use brain scanning technologies to
investigate whether biochemical processes differ
between the two study strategies. At a higher level
—the level of thinking—we might investigate
processes of decision making (what to study) and
ability to focus, as they relate to cramming versus
spaced practice. At even higher levels, we might
be interested in real world behaviors, such as how
long people study using each of the strategies.
Similarly, we might be interested in how the
presence of others influences learning across
these two strategies. Levels of analysis suggests
that one level is not more correct—or truer—than
another; their appropriateness depends on the
specifics of the question asked. Ultimately, levels
of analysis would suggest that we cannot
understand the world around us, including
human psychology, by reducing the phenomenon
to only the biochemistry of genes and dynamics of
neural networks. But, neither can we understand
humanity without considering the functions of the
human nervous system.

Science in Context

There are many ways to interpret the world

around us. People rely on common sense,
personal experience, and faith, in combination
and to varying degrees. All of these offer
legitimate benefits to navigating one’s culture, and
each offers a unique perspective, with specific
uses and limitations. Science provides another
important way of understanding the world and,
while it has many crucial advantages, as with all
methods of interpretation, it also has limitations.
Understanding the limits of science—including its
subjectivity and uncertainty—does not render it
useless. Because it is systematic, using testable,
reliable data, it can allow us to determine
causality and can help us generalize our
conclusions. By understanding how scientific
conclusions are reached, we are better equipped
to use science as a tool of knowledge.

Answer - Test Yourself 1: Can It

Be Falsified?
Answer explained: There are 4 hypotheses
presented. Basically, the question asks “which of
these could be tested and demonstrated to be
false?". We can eliminate answers A, B and C. A is
a matter of personal opinion. C is a concept for
which there are currently no existing measures.
B is a little trickier. A person could look at data
on wars, assaults, and other forms of violence to
draw a conclusion about which period is the
most violent. The problem here is that we do not
have data for all time periods, and there is no
clear guide to which data should be used to
address this hypothesis. The best answer is D,
because we have the means to view other
planets and to determine whether there is water
on them (for example, Mars has ice).

Answer - Test Yourself 2:

Inductive or Deductive
Answer explained: This question asks you to
consider whether each of 4 examples represents
inductive or deductive reasoning. 1) Inductive—it
is possible to draw the conclusion—the
homeowner left in a hurry—from specific
observations such as the stove being on and the
door being open. 2) Deductive—starting with a
general principle (gravity is associated with
mass), we draw a conclusion about the moon
having weaker gravity than does the Earth
because it has smaller mass. 3) Deductive—
starting with a general principle (students do not
like to pay for textbooks) it is possible to make a
prediction about likely student behavior (they
will not purchase textbooks). Note that this is a
case of prediction rather than using
observations. 4) Deductive—starting with a
general principle (students need 100 credits to
graduate) it is possible to draw a conclusion
about Janine (she cannot graduate because she
has fewer than the 100 credits required).

Outside Resources

Article: A meta-analysis of research on combating

mis-information
http://journals.sagepub.com/doi/full/10.1177/0956
797617714579

Article: Fixing the Problem of Liberal Bias in Social

Psychology
https://www.scientificamerican.com/article/fixing-
the-problem-of-liberal-bias-in-social-psychology/

Article: Flat out science rejection is rare, but

motivated rejection of key scientific claims is
relatively common.
https://blogs.scientificamerican.com/guest-
blog/who-are-you-calling-anti-science/

Article: How Anecdotal Evidence Can Undermine

Scientific Results
https://www.scientificamerican.com/article/how-
anecdotal-evidence-can-undermine-scientific-
results/

Article: How fake news is affecting your memory

http://www.nature.com/news/how-facebook-fake-
news-and-friends-are-warping-your-memory-
1.21596

Article: New Study Indicates Existence of Eight

Conservative Social Psychologists
https://heterodoxacademy.org/2016/01/07/new-
study-finds-conservative-social-psychologists/

Article: The Objectivity Thing (or, Why Science Is a

Team Sport).
https://blogs.scientificamerican.com/doing-good-
science/httpblogsscientificamericancomdoing-
good-science20110720the-objectivity-thing-or-
why-science-is-a-team-sport/

Article: Thomas Kuhn: the man who changed the

way the world looked at science
https://www.theguardian.com/science/2012/aug/1
9/thomas-kuhn-structure-scientific-revolutions

Video: Karl Popper's Falsification - Karl Popper

believed that human knowledge progresses
through 'falsification'. A theory or idea shouldn't
be described as scientific unless it could, in
principle, be proven false.

Karl Popper's Falsification

Video: Karl Popper, Science, and Pseudoscience:

Crash Course Philosophy #8

Karl Popper, Science, & Pseudo…

Pseudo…

Video: Simple visualization of Type I and Type II

errors

False Positives, False Negative…

Negative…

Web: An overview and history of the concept of

fake news.
https://en.wikipedia.org/wiki/Fake_news

Web: Heterodox Academy - an organization

focused on improving "the quality of research and
education in universities by increasing viewpoint
diversity, mutual understanding, and constructive
disagreement".
https://heterodoxacademy.org/

Web: The People's Science - An orgnization

dedicated to removing barriers between scientists
and society. See examples of how researchers,
including psychologists, are sharing their research
with students, colleagues and the general public.
http://thepeoplesscience.org/science-
topic/human-sciences/

Discussion Questions

1. When you think of a “scientist,” what image

comes to mind? How is this similar to or
different from the image of a scientist
described in this module?

2. What makes the inductive reasoning used in

the scientific process different than the
inductive reasoning we employ in our daily
lives? How do these differences influence our
trust in the conclusions?

3. Why aren’t horoscopes considered scientific?

4. If science cannot “prove” something, why do

you think so many media reports of scientific
research use this word? As an educated
consumer of research, what kinds of questions
should you ask when reading these secondary
reports?

5. In thinking about the application of research in

our lives, which is more meaningful: individual
research studies and their conclusions or
scientific theories? Why?

6. Although many people believe the conclusions

offered by science generally, there is often a
resistance to specific scientific conclusions or
findings. Why might this be?

Vocabulary

Anecdotal evidence
A piece of biased evidence, usually drawn from
personal experience, used to support a
conclusion that may or may not be correct.

Causality
In research, the determination that one variable
causes—is responsible for—an effect.

Correlation
In statistics, the measure of relatedness of two or
more variables.

Data (also called observations)

In research, information systematically collected
for analysis and interpretation.

Deductive reasoning
A form of reasoning in which a given premise
determines the interpretation of specific
observations (e.g., All birds have feathers; since a
duck is a bird, it has feathers).

Distribution
In statistics, the relative frequency that a
particular value occurs for each possible value of
a given variable.

Empirical
Concerned with observation and/or the ability to
verify a claim.

Fact
Objective information about the world.

Falsify
In science, the ability of a claim to be tested and—
possibly—refuted; a defining feature of science.

Generalize
In research, the degree to which one can extend
conclusions drawn from the findings of a study to
other groups or situations not included in the
study.

Hypothesis
A tentative explanation that is subject to testing.

Induction
To draw general conclusions from specific
observations.

Inductive reasoning
A form of reasoning in which a general conclusion
is inferred from a set of observations (e.g., noting
that “the driver in that car was texting; he just cut
me off then ran a red light!” (a specific
observation), which leads to the general
conclusion that texting while driving is
dangerous).

Levels of analysis
In science, there are complementary
understandings and explanations of phenomena.

Null-hypothesis significance testing (NHST)

In statistics, a test created to determine the
chances that an alternative hypothesis would
produce a result as extreme as the one observed
if the null hypothesis were actually true.

Objective
Being free of personal bias.

Population
In research, all the people belonging to a
particular group (e.g., the population of left
handed people).

Probability
A measure of the degree of certainty of the
occurrence of an event.

Probability values
In statistics, the established threshold for
determining whether a given value occurs by
chance.

Pseudoscience
Beliefs or practices that are presented as being
scientific, or which are mistaken for being
scientific, but which are not scientific (e.g.,
astrology, the use of celestial bodies to make
predictions about human behaviors, and which
presents itself as founded in astronomy, the
actual scientific study of celestial objects.
Astrology is a pseudoscience unable to be
falsified, whereas astronomy is a legitimate
scientific discipline).

Representative
In research, the degree to which a sample is a
typical example of the population from which it is
drawn.

Sample
In research, a number of people selected from a
population to serve as an example of that
population.

Scientific theory
An explanation for observed phenomena that is
empirically well-supported, consistent, and fruitful
(predictive).

Type I error
In statistics, the error of rejecting the null
hypothesis when it is true.

Type II error
In statistics, the error of failing to reject the null
hypothesis when it is false.

Value
Belief about the way things should be.

References

Kuhn, T. S. (2012). The structure of scientific

revolutions: 50th anniversary edition.
Chicago, USA: University of Chicago Press.

Kuhn, T. S. (2011). Objectivity, value judgment,

and theory choice, in T. S. Kuhn (Ed.), The
essential tension: Selected studies in scientific
tradition and change (pp. 320-339). Chicago:
University of Chicago Press. Retrieved
from http://ebookcentral.proquest.com

Authors

Erin I. Smith
Erin I. Smith is Associate
Professor of Psychology at
California Baptist
University. She earned her
PhD in Developmental
Psychology at the
University of California,
Riverside. She was
recently a visiting scholar
in science and religion
with SCIO (Scholarship
and Christianity in Oxford)
and currently serves as
the director for the Center
for the Study of Human
Behavior at CBU.

Creative Commons License

Thinking like a Psychological Scientist by Erin I. Smith is

licensed under a Creative Commons Attribution-
NonCommercial-ShareAlike 4.0 International License.
Permissions beyond the scope of this license may be
available in our Licensing Agreement.

How to cite this Noba module

using APA Style

I. Smith, E. (2025). Thinking like a psychological

scientist. In R. Biswas-Diener & E. Diener (Eds),
Noba textbook series: Psychology. Champaign, IL:
DEF publishers. Retrieved from
http://noba.to/nt3ysqcm

SECTIONS

Abstract

Learning Objectives

Introduction

Scientific Versus Everyday Reasoning

Test Yourself 1: Can It Be Falsified?

The Interpretation of Research Results

Test Yourself 2: Inductive or Deductive?

Why Should I Trust Science If It Can’t Prove A…

Scientific Theories

Is Science Objective?

Science in Context

Answer - Test Yourself 1: Can It Be Falsified?

Answer - Test Yourself 2: Inductive or Deductive

Outside Resources

Discussion Questions

Vocabulary

References

Authors

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