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Stereotype threat

type: effect · origin: study · evidence: failed to replicate · themes: how we judge others

Claim assessed: Being reminded of a negative stereotype about your group lowers your test results.

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In short

Stereotype threat is the idea that people from a group with a negative stereotype do worse on a test when the stereotype is brought up, because they fear confirming it. It would mean that part of the score differences between groups comes not from ability but from the testing situation. The idea was very influential, made it into textbooks and was even cited in court cases about university admissions. Recent large, preregistered replications, however, no longer found the effect.

What it says

The classic studies. Steele and Aronson (1995) reported that Black students scored lower on a difficult test when it was presented as a measure of intellectual ability. Spencer et al. (1999) reported a similar effect in women, in maths. We haven’t read them; we describe them from the replication report below.

The replicated study. Johns, Schmader and Martens (2005) gave students a maths test. For some, the test was presented as a way to establish different norms for men and women; for others, as a problem-solving task, or they were told in advance that anxiety might come from the stereotype rather than a lack of ability. Women scored lower only in the first version. The study has been cited hundreds of times. We haven’t read it; we describe it from the replication report.

The meta-analysis on girls. Flore and Wicherts (2015) gathered 47 effects from studies of girls under 18, in maths, science and spatial skills. They found a small to moderate average effect (d = −0.22), but also several signs of selective publication that could seriously distort the literature. They proposed a large replication.

The effect size of stereotype threat on women's maths scores. Johns and colleagues' 2005 study: minus 0.82. Flore and Wicherts' meta-analysis on girls: minus 0.22, with signs of selective publication. The registered replication in nine labs: plus 0.04, practically zero. From a large effect to almost zero Johns et al., 2005; Stoevenbelt et al., 2025 Effect on the maths score (d) negative: worse under threat The original study 45 women −0.82 Meta-analysis, girls under 18 47 effects, signs of selective publication −0.22 Registered replication, 9 labs 1,275 women +0.04 In the large replication, the effect in women was practically zero.
Johns et al. (2005) and the replication by Stoevenbelt et al. (2025), as reported in the replication report. Flore and Wicherts (2015), from the abstract. The measures aren't identical; compare the direction and order of magnitude.

The replication in nine labs. Stoevenbelt, Flore, Wicherts and their collaborators (2025) repeated Johns et al.’s study in nine labs in the Netherlands, Austria, Germany, Slovakia and the US, with 1,709 participants, following a plan registered in advance. Women scored somewhat lower than men on average, but that didn’t depend on how the test was presented. The threat effect in women was practically zero (g = 0.04), compared with d = −0.82 in the original study, which had 45 women. The effect didn’t appear in the American labs either. The authors note that the result fits other preregistered replications that didn’t find the effect.

A real-world test. Inglis and O’Hagan (2022) tested, at a UK maths competition, the idea that the gender question should come at the end of the test rather than the start, so as not to remind girls of the stereotype. With 1,169 students and the analysis registered in advance, the position of the question changed nothing.

Example

Before a maths test, a teacher says: “Let’s see whether the girls do as well as the boys.” The theory predicts that the girls will score lower because of this remark. Large replications suggest that the effect, if it exists, is much smaller than was thought. The example is an illustration, not a study cited here.

How to apply it

The steps below are a practical way to apply it that we propose, based on the studies cited.

  1. Don’t explain group differences by stereotype threat. Current evidence doesn’t support it explaining an important part of these differences.
  2. Still avoid remarks about stereotypes at tests. Even if the measured effect is close to zero, there is no good reason to make them.
  3. Don’t base policies on simple fixes like “move the gender question”. Inglis and O’Hagan found no effect of this change.
  4. When you hear of a spectacular effect, ask about preregistered replications. This case resembles priming: large effects in small studies, almost zero in large ones.

Limits and nuances

Sources

See also: Growth mindset, Priming, Pygmalion effect