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In this video, we'll differentiate

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between stereotypes, prejudice, [br]and discrimination;

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and we'll discuss several important[br]social psychological concepts

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and hypotheses related to each,

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including what causes them [br]to arise in the first place.

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Let's go over a bit of [br]terminology to kick things off.

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A stereotype is a belief [br](which can be positive or negative )

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about the characteristics [br]of members of a group

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that is applied generally [br]to most members of that group.

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Believing that Asians are good [br]at math, for example, is positive;

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it's not necessarily derogatory,

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but it's nonetheless a stereotype [br]that you have about Asians.

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Now, stereotypes (these beliefs) can [br]lead to prejudice, which in contrast,

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can only ever be negative.

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Prejudice involves drawing [br]negative conclusions about

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a person, a group of people,

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or a situation prior to [br]evaluating the evidence.

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These baseless conclusions are typically

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the result of those stereotypes [br]that you hold about that group.

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Also, in contrast to stereotypes,

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prejudice involves emotion; [br]it’s an attitude.

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Being prejudiced against a person [br]or a group of people involves

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feeling negatively toward them.

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Now, because of these negative emotions

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and these negative conclusions [br]that you're coming to,

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prejudice often leads to discrimination,

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which is negative behavior [br]towards members of an out-group.

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And by the way, an out-group is a group

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that we don't belong to or one that we [br]view as fundamentally different from us;

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whereas an in-group, in contrast,

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refers to a group that we DO identify [br]with or see ourselves as belonging to.

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So I might be using that [br]terminology quite a bit–

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important to know.

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So just to summarize,

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stereotypes are beliefs,[br]prejudice is an attitude,

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and discrimination is a behavior.

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Let's go over an example [br]that puts all of this together.

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Let's say, for example, that you [br]believe older adults are incompetent,

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and that's a stereotype that [br]you have about older adults.

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(And I'll note that I'm not endorsing [br]this stereotype or any other stereotype

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that I use as an example in this video,

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but we have to have some kind [br]of an example to work with here.)

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So let's say you work at, [br]I don't know, a tech company

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and you're looking to hire an assistant.

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If an elderly gentleman applies,

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you might walk into that interview [br]with the gentleman,

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assuming he won't be a good fit [br]or that he'd be difficult to train.

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Now, we would call this [br]premature conclusion;

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this negative attitude toward [br]this gentleman [is] prejudice.

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Finally, you may decide not [br]to hire the gentleman at all

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because of your stereotype,[br]because of your prejudice.

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In this case, the behavior [br]of not hiring him

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would be discrimination.

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Now, stereotypes and prejudice [br]can be either explicit

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(meaning, we're consciously [br]aware of having this bias)

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or implicit (meaning, it's there, [br]but we aren't aware of it).

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Research shows that explicit prejudice [br]is in decline, which is encouraging;

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however, implicit prejudice [br]really isn't much.

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That is, people report being [br]very anti-bias nowadays,

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but their behavior still [br]tells us a different story.

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Let's take a look at a few [br]examples to illustrate.

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Starting with the realm of gender, [br]we can look to some of my own data.

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In one study, I searched through

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the language used by students [br]evaluating their teachers

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in over 14 million reviews posted to [br]a popular instructor evaluation website,

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RateMyProfessors.com,[br]which you've perhaps used in the past.

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I was specifically interested in[br]stereotypes about intelligence,

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so I searched through uses of [br]the words “genius” and “brilliant.”

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So let's take a look at the results.[br]There's a lot of information here.

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Let me help you interpret these graphs.

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These are graphs for uses of

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the words “genius” on the left [br]and “brilliant” on the right.

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The x-axis on both of these graphs

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represents uses per millions [br]of words of text,

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which might sound a little [br]complicated, but really isn't.

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There's a ton of text here,

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so to keep the numbers on the [br]x-axis from being enormous

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and just visually unappealing,

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I used this uses per millions of words of text, [br]but the interpretation is basically the same.

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The further to the right[br]you go on the x-axis

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(the higher the number), [br]the more this word was used.

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So that's how you can interpret that.

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The y-axis here displays all of the [br]different fields such as philosophy,

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music, mathematics, psychology;

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so you can look for your own field

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or just pause the video and look through[br]them in general, if you're curious,

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And fields that are higher up [br]on the y-axis were the ones

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in which the words were used the most often.

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The blue dots here on the slide [br]represent reviews of male professors,

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whereas the orange dots represent[br]reviews of female professors.

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Before I give you the punch line, [br]what do you notice here?

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Well, what I found is that every [br]field for which we have data,

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students describe their male [br]professors as genius and brilliant

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significantly more often than [br]they do their female professors.

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And in no field was this effect reversed,

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even for fields where women [br]were the statistical majority.

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And this points to a stereotype [br]in favor of men's intelligence

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and against women's intelligence.

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You might be wondering:

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Does this reflect an overall[br]bias against women,

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or is the stereotype specific [br]to intellectual ability?

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Well, I was curious about this as well,

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but if you look at the data for [br]the terms “excellent” and “amazing,”

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the gender bias goes away entirely.

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It appears that students believe

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that their female professors [br]can be excellent and amazing,

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but they believe it's mainly the male [br]professors who are genius and brilliant.

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Again, this is evidence of implicit bias

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because students are likely

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not consciously aware[br]of this discrepancy.

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They're simply going on line[br]to review their professors

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and they're not giving their [br]stereotypes any thought.

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So explicitly, students would [br]likely say they don't hold a bias,

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yet implicitly, they respond in this way.

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This is a common theme

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in modern research on stereotypes, [br]prejudice, and discrimination.

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Now that's gender.[br]What about race?

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One study found that doctors [br]were only 60% as likely to suggest

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a top-rated diagnostic test [br]for Black heart patients

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than for White heart patients.

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There's also evidence to suggest

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that White men are offered [br]greater financial opportunities.

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As one example, a study found [br]that White men were offered

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the best deals at used car dealerships.

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White men paid $109 on average[br]less than White women,

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$318 less than Black women,

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and a whopping $935 less for a [br]used car on average than Black men.

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Now, these are just two examples out [br]of thousands that I could tell you about,

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but again, it's likely the case

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that these doctors and car salesmen[br]aren't EXPLICITLY biased,

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but their behavior provides [br]evidence of IMPLICIT bias.

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Okay, so let's finish [br]with a brief discussion

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of what leads to the development and [br]perpetuation of some of these things

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(stereotypes, prejudice,[br]and discrimination),

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starting with stereotypes.

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A factor that we've learned about [br]before is confirmation bias,

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the tendency to seek out evidence [br]that supports our beliefs

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and to deny, dismiss, or distort [br]evidence that contradicts them.

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Say, for example that you believe [br]women to be bad drivers.

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If you're out driving for an hour,

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you might encounter several bad [br]drivers, some male, some female.

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If you don't have a stereotype [br]against male drivers, though,

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you might not think much of them [br]when they speed or make dangerous moves.

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But the second a female driver [br]cuts you off, for example,

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you feel vindicated as though [br]you've found additional evidence

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or proof for your belief.

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And this reinforces your stereotype

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even though, in truth, many people are [br]bad drivers regardless of their gender.

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Now, if we used System 2 thinking

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(which we've learned [br]about in a previous video)

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to evaluate these kinds of assumptions [br]and the data that we base them on,

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we might realize that those assumptions [br]are erroneous, but we usually don't.

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This is because we are cognitive misers.

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That is, we seek to use only [br]minimal cognitive resources

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when explaining the world around us.

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Evaluating our stereotypes takes effort,

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and because we generally [br]don't go to more effort

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than we deem absolutely necessary,

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we don't evaluate or re-evaluate them at all.

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Now, what causes prejudice?

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First, we have in-group bias, [br]which refers to the tendency

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to favor individuals from within our [br]group over those from outside our group.

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Evidence from developmental [br]psychology suggests that this bias

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is innate, with young infants showing[br]strong preferences, for example,

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for others who share their preferences[br](such as their favorite snack)

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and infants disliking others who [br]do not share their preferences

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(for example, if the other person shows[br]that they like a different snack more).

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Think of the implications [br]for racism, sexism, and so on.

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Another factor is called [br]the ultimate attribution error,

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which refers to the assumption

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that behaviors among [br]individual members of a group

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are due to their internal dispositions.

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Out-group members’ flaws [br]are due to internal factors

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such as their personality or their race, [br]whereas in-group members flaws aren't.

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This might sound a lot like [br]the fundamental attribution error,

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which we've learned about before, [br]but it is a bit different.

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Think of the ultimate attribution error

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as more of a narrow case of [br]the fundamental attribution error

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applied specifically to attributions

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about an individual in relation to[br]the group to which they belong.

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Along similar lines,

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out-group homogeneity [br]refers to the tendency to view

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all individuals outside our group [br]as highly similar to one another.

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Here, think of the implications

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for identifying a suspect in [br]a police lineup, for example,

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but also consider this bias in relation [br]to the ultimate attribution error.

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It's a very bad combination to assume

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that out-group members flaws [br]are due to inherent factors

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such as their personalities or their race,

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and to simultaneously assume

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that out-group members are all [br]highly similar to one another.

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Finally, scapegoating refers to [br]the act of blaming an out-group

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when the in-group experiences frustration or [br]is blocked from obtaining some kind of a goal.

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People scapegoat because it preserves [br]a positive self-concept.

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If you believe the reason you can't get a job [br]is because immigrants are taking them all,

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well, then you don't have to [br]come to terms with the reality

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that you simply aren't qualified or [br]competent enough for that line of work.

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Now, this list of causes here [br]is by no means all-inclusive

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but should give you a good idea of [br]the general psychological phenomena

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that lead to the formation and[br]perpetuation of stereotypes,

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prejudice, and discrimination. [END]