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Character  &  Context

Are Stereotypes Accurate? A Viewpoint from the Cognitive Science of Concepts

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By Lin Bian and Andrei Cimpian 

THE GIST (tl;dr)

In his book Social Perception and Social Reality, Lee Jussim suggests that people’s beliefs about various groups (i.e., their stereotypes) are largely accurate. We unpack this claim using the distinction between generic and statistical beliefs—a distinction supported by extensive evidence in cognitive psychology, linguistics, and philosophy. Regardless of whether one understands stereotypes as generic or statistical beliefs about groups, skepticism remains about the rationality of social judgments.



Lee Jussim is doing psychology a service by prompting careful thinking about a number of topics. We will focus our comments on his arguments about stereotypes, which he defines as “beliefs about the attributes of social groups” (Jussim, in press, p. 34). Going against the seeming consensus in social psychology, Jussim suggests that stereotypes are largely accurate. Here, we unpack this claim using a conceptual distinction from the cognitive psychology of concepts (used widely in linguistics and philosophy as well): namely, the distinction between generic and statistical beliefs about a category (e.g., Carlson & Pelletier, 1995; Cimpian, Brandone, & Gelman, 2010; Leslie, Khemlani, & Glucksberg, 2011; Prasada, 2000). Attending to this distinction allows a more precise analysis of the claim that stereotypes are accurate—an analysis that ultimately undermines this claim. As we explain below, if one defines stereotypes as generic beliefs about groups, then the evidence Jussim presents (all of which pertains to people’s statistical estimates about various group attributes) is largely irrelevant to their accuracy. By virtue of their very structure, generic beliefs have only a weak relation to the statistical criteria that Jussim uses to define accuracy. On the other hand, if one defines stereotypes as statistical beliefs about groups, then one may no longer be speaking to the bulk of social judgments. The literature on concepts suggests it is generic—not statistical—beliefs that people use most readily when reasoning about categories and their members. Thus, regardless of how one unpacks Jussim’s claims on this topic, the accuracy of people’s judgments about groups is still in doubt. In what follows, we first outline the distinction between generic and statistical beliefs and then proceed to discuss its implications for Jussim’s arguments.


1. The distinction between generic and statistical beliefs about categories

To begin, consider the statements below:

(1a) Fewer than 1% of mosquitoes carry the West Nile virus.

(1b) Mosquitoes carry the West Nile virus.

(2a) The majority of books are paperbacks.

(2b) Books are paperbacks.

Statements (1a) and (2a) are statistical: They express a belief about a certain number or proportion of the members of a category. Statements (1b) and (2b) are generic: They express a belief about the category as a whole rather than a specific number, quantity, or proportion. (An easy way to check the latter claim is to try out each of these statements as an answer to a “How many?” question. Only the statistical statements will sound appropriate.)

The fact that generic claims – and the beliefs they express – are not about numbers or quantities has a crucial consequence: It severs their truth conditions from the sort of statistical data that one could objectively measure in the world. In other words, whether people judge a generic belief about a category to be true does not straightforwardly depend on how many members of that category display the relevant attribute. This point is illustrated by the examples above. Both (1a) and (1b) are considered true: Although very few mosquitoes actually carry the West Nile virus, participants judge the generic claim (that mosquitoes[i], as a category, carry the West Nile virus) to be true as well (e.g., Prasada, Khemlani, Leslie, & Glucksberg, 2013). In contrast, even though (2a) is true – paperbacks are indeed very common – few believe that books, as a category, are paperbacks (i.e., [2b] is false). Notably, these are not isolated examples. The literature is replete with instances of generic claims that either are judged true despite unimpressive statistical evidence or judged false despite overwhelming numbers (e.g., Carlson & Pelletier, 1995; Leslie, 2007, 2008). In fact, the rules that govern which generic beliefs are deemed true and which are deemed false are so baroque and so divorced from the statistical facts that many linguists and philosophers have spent the better part of 40 years debating them (e.g., Carlson & Pelletier, 1995; Lawler, 1973; Leslie, 2008).

Importantly, all of the foregoing applies to beliefs about social groups as well (e.g., Cimpian & Markman, 2011; Cimpian, Mu, & Erickson, 2012; Gelman, Taylor, & Nguyen, 2004; Leslie, 2008, in press; Prasada & Dillingham, 2006, 2009; Rhodes, Leslie, & Tworek, 2012). The distinction between statistical and generic beliefs is operative regardless whether these beliefs concern mosquitoes, books, and other categories of non-human entities, or women, African Americans, Muslims, and other categories of humans. This means that generic beliefs about social groups, just like other generic beliefs, are typically removed from the underlying statistics. For example, more people hold the generic belief that Muslims are terrorists than hold the generic belief that Muslims are female (see Leslie, in press; Leslie et al., 2011). However, there are vastly more Muslims who are female than there are Muslims who are terrorists. Most of us would even be able to report these statistics, as Jussim’s own data suggest; yet, awareness of the statistics has little bearing on endorsement of the respective generic beliefs. Again, this is not an isolated example. Compare, for instance, “Asians are really good at math” and “Asians are right-handed.” Many more people would agree with the former generic claim than with the latter, while simultaneously being aware that the statistics go the opposite way.

In summary, people’s beliefs about categories are of two types: generic and statistical. Although the accuracy of statistical beliefs depends solely on the data available in the world (e.g., how many Muslims are terrorists vs. women), the judged truth of generic beliefs does not. Rather, generic beliefs can be – and often are – largely discrepant with the reality on the ground.


2. Implications for the argument that stereotypes are accurate

We now go on to spell out the implications of this body of work for Jussim’s argument. Regardless of which sort of belief (generic or statistical) he had in mind when claiming that stereotypes are largely accurate – and we will discuss each possibility in turn – the force of his argument is considerably weakened by attending to the evidence presented above.

2.1. Stereotypes as generic beliefs. Let’s first assume that stereotypes are generic beliefs about groups. Based on our reading of the literature, this is how many social psychologists conceive of stereotypes, even though they understandably don’t use the term generic. (Actually, at least one social psychologist we know of did use the term: Bob Abelson, whose research team published some fascinating work on “generic assertions” about social groups in the 1960s [e.g., Abelson & Kanouse, 1966; Gilson & Abelson, 1965].)

If stereotypes are generic beliefs, the evidence Jussim presents – all of which is about people’s statistical estimates concerning group attributes – does not legitimize the claim that stereotypes are accurate. As explained above, generic beliefs depend only in a loose sense on the statistics available in the world. As a result, one cannot justifiably claim that generic beliefs are accurate, at least using the commonsense notion of accuracy that Jussim himself operates with. Is it accurate to believe – as most people do – that mosquitoes carry the West Nile virus but not that books are paperbacks? Is it accurate to believe that Asians are good at math but not right-handed? Sure, these beliefs may be “accurate” in the sense of being endorsed by many people, but agreement is a poor substitute for accuracy. The accuracy of a belief about a group is more legitimately assessed, as Jussim does, in terms of whether it matches the world statistically.

Based on these considerations, it seems reasonable to claim that, if stereotypes are generic beliefs, then they are often inaccurate – out of touch with the statistical reality. This is, of course, what many social psychologists have been claiming all along, and their claims appear justified under this definition of stereotypes. To further drive home this point, we briefly lay out four types of evidence suggesting considerable inaccuracy in people’s (generic) stereotypes.

2.1.1. Generic beliefs are often endorsed on the basis of scant statistical evidence. As already illustrated, many common generic beliefs are about attributes that are infrequent (e.g., Muslims are terrorists). Notably, generic beliefs based on limited statistical evidence have also been observed in more controlled settings – for example, in laboratory studies where participants were given information about the prevalence of various traits in unfamiliar categories and then tested for their endorsement of the corresponding generic beliefs (e.g., Brandone, Gelman, & Hedglen, 2015; Cimpian et al., 2010). Thus, several types of evidence (obtained with participants spanning the range from 4-year-olds to adults) suggest a disconnect between endorsement of generic beliefs and the underlying statistical facts.

2.1.2. Generic beliefs are resistant to counterevidence. Related to the point about weak dependence on statistical evidence, once a generic belief is adopted, it is not easily falsified by exposure to evidence that contradicts it. The generic belief that mosquitoes carry the West Nile virus is not discarded as soon as a mosquito bite (or tens, or hundreds) fails to infect us. (The same goes for law-abiding Muslims and Asian people who aren’t good at math.) Experimental work supports this conclusion as well. For example, 4-year-olds who first heard that pagons (an unfamiliar category) are friendly and were then shown a counterexample ended up generalizing this trait to novel pagons as frequently as children who did not see the counterexample (Chambers, Graham, & Turner, 2008), which suggests that the counterexample had no effect on their endorsement of the generic belief.

2.1.3. Generic beliefs give the impression of strong statistical support. Even though generic beliefs are often adopted on the basis of little statistical evidence, they nevertheless suggest – e.g., when expressed in conversation – that the relevant attributes are almost always present (Brandone et al., 2015; Cimpian et al., 2010). For example, imagine a person who wasn’t familiar with how the West Nile virus is transmitted. What would this person infer if they heard that mosquitoes carry it? Would they assume that fewer than 1% of mosquitoes in the affected areas are carriers, or perhaps that many more – even a majority – are? The evidence supports the latter possibility. In fact, most participants assume prevalence levels of greater than 90% when exposed to unfamiliar generic facts (Cimpian et al., 2010). There is thus a stark asymmetry at the core of generic beliefs: Although they are largely independent of the underlying statistics at the stage when they are initially formulated, they immediately take on the appearance of being rooted in strong statistical uniformities. For anyone who has little firsthand familiarity with the actual facts, this asymmetry can lead to largely mistaken impressions about the state of the world.

2.1.4. Generic beliefs are accompanied by misleading explanatory intuitions. Generic beliefs have strong explanatory overtones. Specifically, generic claims are consistently interpreted as conveying deep, inherent properties of the relevant categories (e.g., Cimpian & Cadena, 2010; Cimpian & Erickson, 2012; Cimpian & Markman, 2009, 2011; Gelman, Ware, & Kleinberg, 2010; Rhodes et al., 2012; see also Cimpian & Salomon, 2014). Thus, when we are exposed to generic beliefs about, say, women being bad at math or African Americans being violent, we are seldom neutral as to the source of the attributes described. Rather, we implicitly adopt an explanatory perspective on these attributes, viewing them as core, non-accidental aspects of what the relevant groups are like deep down. To the extent that many group characteristics are not actually due to their members’ biological makeup, this explanatory component of generic beliefs provides additional reasons to be suspicious of their match with the world.

In summary, if stereotypes are conceived as generic beliefs, then the evidence suggests they display a considerable amount of inaccuracy.

2.2. Stereotypes as statistical beliefs. What if we defined stereotypes as statistical beliefs instead? In this case, the evidence Jussim presents seems consistent with the idea that stereotypes are largely accurate. While that may be so, committing to a definition of stereotypes as statistical beliefs about groups may be problematic for another reason: A recent (yet already widely replicated) finding in the literature on concepts suggests that people often have difficulty reasoning with (manipulating, basing inferences on, etc.) statistical knowledge about categories (e.g., Gelman, Tapia, & Leslie, in press; Hampton, 2012; Hollander, Star, & Gelman, 2002; Khemlani, Leslie, & Glucksberg, 2012; Jönsson & Hampton, 2006; Leslie et al., 2011; Leslie & Gelman, 2012; Meyer, Gelman, & Stilwell, 2011). In many circumstances, people tend to fall back on using generic representations, consistent with theoretical arguments that such representations are an easy “default” when reasoning about categories (e.g., Cimpian & Erickson, 2012; Gelman, 2003; Leslie, 2008). Thus, even if people are at some level attuned to the statistical distributions of various attributes across various groups, such statistical knowledge may ultimately be less influential than people’s generic beliefs about the same attributes.

We illustrate this point with data from Khemlani et al. (2012), who measured people’s expectations about the traits of unfamiliar category members – a ubiquitous type of social judgment (e.g., how strongly do you expect the next Asian person you’ll meet to be good at math?). Khemlani et al.’s goal was to compare the extent to which these expectations are rooted in participants’ statistical estimates (e.g., what percentage of Asian people do you think are good at math?) vs. their generic beliefs (e.g., do you believe that Asians are good at math?).[ii] The results highlighted the powerful influence of generic beliefs. Although participants’ statistical estimates did explain unique variance in their expectations about unfamiliar individuals, their endorsement of the relevant generic beliefs was considerably more predictive of these judgments (with an effect size that was 53% larger). Based on this and other similar evidence, we suggest that people’s awareness of the statistical distributions of various traits may be less important to their social judgments than their generic beliefs are. Further research testing this (admittedly bold) claim would be in order, however.

In summary, if stereotypes are conceived as statistical beliefs, they may not provide as much insight into people’s actual social judgments as one might expect.


3. Conclusion

Stereotypes are generic or statistical beliefs about the attributes of groups. If they are generic, they are likely not very accurate. If they are statistical, they may not be as influential as our (often inaccurate) generic beliefs about groups. Either way, one remains skeptical about the rationality of everyday social judgment.


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[i] For added clarity, we will occasionally use small caps to indicate when we intend to refer to categories.

[ii] Khemlani et al. (2012) didn’t include this specific item, but we use it here for consistency.

Lin Bian is a PhD student in developmental psychology at the University of Illinois. Andrei Cimpian (@AndreiCimpian) is an associate professor of psychology at the University of Illinois.

Lee Jussim is a social psychologist and former department chair at Rutgers University.  He headed up the Scientific Integrity/Best Practices in Science Group at Stanford’s Center for Advanced Study in the Behavioral Sciences (2013-2015).  His , Social perception and social reality: Why accuracy dominates self-fulfilling prophecy and bias (Oxford University Press) received the American Publisher’s Association award for best book in psychology of 2012.  Much of his recent work has focused on how faculty politics and confirmation biases distort social science conclusions.

A version of this commentary will be published in an upcoming issue of the journal Behavioral and Brain Sciences. 

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