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In this video, we'll differentiate
between stereotypes, prejudice, and discrimination;
and we'll discuss several important social psychological concepts
and hypotheses related to each,
including what causes them to arise in the first place.
Let's go over a bit of terminology to kick things off.
A stereotype is a belief (which can be positive or negative )
about the characteristics of members of a group
that is applied generally to most members of that group.
Believing that Asians are good at math, for example, is positive;
it's not necessarily derogatory,
but it's nonetheless a stereotype that you have about Asians.
Now, stereotypes (these beliefs) can lead to prejudice, which in contrast,
can only ever be negative.
Prejudice involves drawing negative conclusions about
a person, a group of people,
or a situation prior to evaluating the evidence.
These baseless conclusions are typically
the result of those stereotypes that you hold about that group.
Also, in contrast to stereotypes,
prejudice involves emotion; it’s an attitude.
Being prejudiced against a person or a group of people involves
feeling negatively toward them.
Now, because of these negative emotions
and these negative conclusions that you're coming to,
prejudice often leads to discrimination,
which is negative behavior towards members of an out-group.
And by the way, an out-group is a group
that we don't belong to or one that we view as fundamentally different from us;
whereas an in-group, in contrast,
refers to a group that we DO identify with or see ourselves as belonging to.
So I might be using that terminology quite a bit–
important to know.
So just to summarize,
stereotypes are beliefs, prejudice is an attitude,
and discrimination is a behavior.
Let's go over an example that puts all of this together.
Let's say, for example, that you believe older adults are incompetent,
and that's a stereotype that you have about older adults.
(And I'll note that I'm not endorsing this stereotype or any other stereotype
that I use as an example in this video,
but we have to have some kind of an example to work with here.)
So let's say you work at, I don't know, a tech company
and you're looking to hire an assistant.
If an elderly gentleman applies,
you might walk into that interview with the gentleman,
assuming he won't be a good fit or that he'd be difficult to train.
Now, we would call this premature conclusion;
this negative attitude toward this gentleman [is] prejudice.
Finally, you may decide not to hire the gentleman at all
because of your stereotype, because of your prejudice.
In this case, the behavior of not hiring him
would be discrimination.
Now, stereotypes and prejudice can be either explicit
(meaning, we're consciously aware of having this bias)
or implicit (meaning, it's there, but we aren't aware of it).
Research shows that explicit prejudice is in decline, which is encouraging;
however, implicit prejudice really isn't much.
That is, people report being very anti-bias nowadays,
but their behavior still tells us a different story.
Let's take a look at a few examples to illustrate.
Starting with the realm of gender, we can look to some of my own data.
In one study, I searched through
the language used by students evaluating their teachers
in over 14 million reviews posted to a popular instructor evaluation website,
RateMyProfessors.com, which you've perhaps used in the past.
I was specifically interested in stereotypes about intelligence,
so I searched through uses of the words “genius” and “brilliant.”
So let's take a look at the results. There's a lot of information here.
Let me help you interpret these graphs.
These are graphs for uses of
the words “genius” on the left and “brilliant” on the right.
The x-axis on both of these graphs
represents uses per millions of words of text,
which might sound a little complicated, but really isn't.
There's a ton of text here,
so to keep the numbers on the x-axis from being enormous
and just visually unappealing,
I used this uses per millions of words of text, but the interpretation is basically the same.
The further to the right you go on the x-axis
(the higher the number), the more this word was used.
So that's how you can interpret that.
The y-axis here displays all of the different fields such as philosophy,
music, mathematics, psychology;
so you can look for your own field
or just pause the video and look through them in general, if you're curious,
And fields that are higher up on the y-axis were the ones
in which the words were used the most often.
The blue dots here on the slide represent reviews of male professors,
whereas the orange dots represent reviews of female professors.
Before I give you the punch line, what do you notice here?
Well, what I found is that every field for which we have data,
students describe their male professors as genius and brilliant
significantly more often than they do their female professors.
And in no field was this effect reversed,
even for fields where women were the statistical majority.
And this points to a stereotype in favor of men's intelligence
and against women's intelligence.
You might be wondering:
Does this reflect an overall bias against women,
or is the stereotype specific to intellectual ability?
Well, I was curious about this as well,
but if you look at the data for the terms “excellent” and “amazing,”
the gender bias goes away entirely.
It appears that students believe
that their female professors can be excellent and amazing,
but they believe it's mainly the male professors who are genius and brilliant.
Again, this is evidence of implicit bias
because students are likely
not consciously aware of this discrepancy.
They're simply going on line to review their professors
and they're not giving their stereotypes any thought.
So explicitly, students would likely say they don't hold a bias,
yet implicitly, they respond in this way.
This is a common theme
in modern research on stereotypes, prejudice, and discrimination.
Now that's gender. What about race?
One study found that doctors were only 60% as likely to suggest
a top-rated diagnostic test for Black heart patients
than for White heart patients.
There's also evidence to suggest
that White men are offered greater financial opportunities.
As one example, a study found that White men were offered
the best deals at used car dealerships.
White men paid $109 on average less than White women,
$318 less than Black women,
and a whopping $935 less for a used car on average than Black men.
Now, these are just two examples out of thousands that I could tell you about,
but again, it's likely the case
that these doctors and car salesmen aren't EXPLICITLY biased,
but their behavior provides evidence of IMPLICIT bias.
Okay, so let's finish with a brief discussion
of what leads to the development and perpetuation of some of these things
(stereotypes, prejudice, and discrimination),
starting with stereotypes.
A factor that we've learned about before is confirmation bias,
the tendency to seek out evidence that supports our beliefs
and to deny, dismiss, or distort evidence that contradicts them.
Say, for example that you believe women to be bad drivers.
If you're out driving for an hour,
you might encounter several bad drivers, some male, some female.
If you don't have a stereotype against male drivers, though,
you might not think much of them when they speed or make dangerous moves.
But the second a female driver cuts you off, for example,
you feel vindicated as though you've found additional evidence
or proof for your belief.
And this reinforces your stereotype
even though, in truth, many people are bad drivers regardless of their gender.
Now, if we used System 2 thinking
(which we've learned about in a previous video)
to evaluate these kinds of assumptions and the data that we base them on,
we might realize that those assumptions are erroneous, but we usually don't.
This is because we are cognitive misers.
That is, we seek to use only minimal cognitive resources
when explaining the world around us.
Evaluating our stereotypes takes effort,
and because we generally don't go to more effort
than we deem absolutely necessary,
we don't evaluate or re-evaluate them at all.
Now, what causes prejudice?
First, we have in-group bias, which refers to the tendency
to favor individuals from within our group over those from outside our group.
Evidence from developmental psychology suggests that this bias
is innate, with young infants showing strong preferences, for example,
for others who share their preferences (such as their favorite snack)
and infants disliking others who do not share their preferences
(for example, if the other person shows that they like a different snack more).
Think of the implications for racism, sexism, and so on.
Another factor is called the ultimate attribution error,
which refers to the assumption
that behaviors among individual members of a group
are due to their internal dispositions.
Out-group members’ flaws are due to internal factors
such as their personality or their race, whereas in-group members flaws aren't.
This might sound a lot like the fundamental attribution error,
which we've learned about before, but it is a bit different.
Think of the ultimate attribution error
as more of a narrow case of the fundamental attribution error
applied specifically to attributions
about an individual in relation to the group to which they belong.
Along similar lines,
out-group homogeneity refers to the tendency to view
all individuals outside our group as highly similar to one another.
Here, think of the implications
for identifying a suspect in a police lineup, for example,
but also consider this bias in relation to the ultimate attribution error.
It's a very bad combination to assume
that out-group members flaws are due to inherent factors
such as their personalities or their race,
and to simultaneously assume
that out-group members are all highly similar to one another.
Finally, scapegoating refers to the act of blaming an out-group
when the in-group experiences frustration or is blocked from obtaining some kind of a goal.
People scapegoat because it preserves a positive self-concept.
If you believe the reason you can't get a job is because immigrants are taking them all,
well, then you don't have to come to terms with the reality
that you simply aren't qualified or competent enough for that line of work.
Now, this list of causes here is by no means all-inclusive
but should give you a good idea of the general psychological phenomena
that lead to the formation and perpetuation of stereotypes,
prejudice, and discrimination. [END]