At the heart of the debate lies a conceptual tension that will be familiar to anyone who studies mental health measurement. The Impostor Phenomenon, the persistent feeling that one’s accomplishments are undeserved and that one is a fraud despite evidence of competence, is not a diagnosis in the way that, say, major depressive disorder is. It is conceptually a continuous construct, distributed along a spectrum across the population. Critics argue that applying the epidemiological language of prevalence to such a construct is conceptually incorrect, because prevalence traditionally refers to the proportion of a population that has a categorical condition. You either have an infection or you do not; you cannot, in the same strict sense, have a fraction of impostorism.

Salari and colleagues do not dispute this premise. In their response, they explicitly agree that the Impostor Phenomenon is continuous and that referring to its prevalence in the strict epidemiological sense may be conceptually wrong. But they argue that this does not rule out threshold-based prevalence estimates, and they point to well-established precedents. Depression and anxiety are also conceptualized as continua, yet researchers routinely estimate their prevalence using instruments such as the PHQ-9 for depression and the GAD-7 for anxiety, which employ empirically derived thresholds to classify individuals above a cutoff. In those fields, decades of validation work have established which scores correspond to clinically meaningful impairment, giving the thresholds legitimacy. The question the exchange raises is whether the Impostor Phenomenon field has earned the same privilege.

The instrument in question is the Clance Impostor Phenomenon Scale, or CIPS, a questionnaire that yields a continuous score of impostor feelings. Primary studies included in the meta-analysis reported the percentage of health service providers scoring above various cutoffs on this scale, effectively equating the proportion of high scorers with prevalence. Salari’s team then pooled those reported proportions into a single estimate. Here the authors make a striking concession: they fully acknowledge that the current cutoffs for the CIPS are inconsistently applied across the existing literature and lack systematic validation. Different studies have used different thresholds, sometimes without empirical justification, meaning that two studies of identical populations could report wildly different prevalence figures depending solely on where they drew the line.

This is precisely the point Brauer and Proyer drove home with an empirical demonstration. By applying six different cutoffs to the same datasets, they showed that the resulting proportions of cases diverged dramatically. They also argued that the extremely high heterogeneity in the meta-analysis, quantified by an I-squared statistic of 98.6 percent, is evidence that pooling prevalence rates in the first place is invalid. In meta-analysis, I-squared describes the percentage of variation across studies that is due to real differences rather than chance; a value near 100 percent means the studies are barely measuring the same thing. To the critics, pooling estimates from studies that use incompatible thresholds produces a number that means little.

Salari and colleagues offer a different reading of that statistic. In their view, the high heterogeneity is itself an important empirical finding, a quantitative portrait of a literature that has operationalized its central construct inconsistently. Meta-analysis, they argue, is precisely the tool that allows researchers to measure and expose this problem, highlighting how different thresholds, samples, and instruments contribute to variance. Without such a synthesis, the field would lack any systematic appreciation of how widely prevalence estimates diverge. They also note that some of the cutoffs applied in the critics’ demonstration were arbitrary and drawn from two convenience datasets that are not population-representative, which they contend artificially inflates the apparent instability and should not be generalized to broader populations. Demonstrating variability across arbitrary thresholds, they write, does not invalidate prevalence research; it underscores the need for consensus and validation studies.

The exchange also touches on a provocative interpretive question. Brauer and Proyer suggested that if 62 to 70 percent of individuals report impostor feelings, as some estimates imply, then the experience should perhaps be regarded as normative rather than pathological. The Salari team counters that high prevalence does not imply triviality. They draw an analogy to physician burnout, which large systematic reviews have shown to be reported by a strikingly high proportion of doctors, yet which has clear and consequential implications for health care delivery. Similarly, they argue, impostor feelings, even if common, are associated with impaired well-being, reduced job satisfaction, and elevated burnout risk. Prevalence estimates, on this view, remain useful for scoping the potential consequences of a widespread experience, regardless of whether it is labeled a disorder.

Notably, the authors point out that their original review contained findings that survive the critique largely intact. Beyond the contested prevalence figures, the meta-analysis summarized correlations between impostor feelings and constructs such as self-esteem, anxiety, depression, stress, and burnout. Because those analyses used continuous scores rather than categorical cutoffs, they are conceptually reliable and less dependent on the threshold decisions that lie at the center of the dispute. This distinction matters for the field’s future direction: the relational findings, which describe how impostorism moves alongside other psychological variables, rest on firmer measurement ground than the headline prevalence numbers.

The response also ventures into a broader reflection on the nature of psychological measurement itself. The authors observe that screening tools for mental health conditions inevitably involve trade-offs, with all instruments producing false positives and false negatives, and that different thresholds can be deployed to compensate for these limitations and optimize screening performance. A screening tool, they emphasize, is not a diagnosis; it flags individuals for further clinical attention. Their stated goals in examining the Impostor Phenomenon were twofold: to draw attention to a condition that has received comparatively little scrutiny, and to provide clinicians with usable information. On that view, the very act of publishing a contested prevalence estimate, and engaging publicly with its critics, serves the scientific process by forcing the field to confront how much its conclusions depend on arbitrary measurement decisions.

Where does this leave the science of feeling like a fraud? The two sides converge more than the heated framing might suggest. Salari’s team agrees that any prevalence estimates should be presented with caution and explicit caveats until robust validation studies are conducted, and they endorse the critics’ implicit agenda: large-scale validation of CIPS thresholds against external criteria, studies using population-representative samples rather than convenience datasets, and community-wide consensus on cutoff standards, modeled on the practices that stabilized depression and anxiety research. The meta-analysis, they argue, does not endorse any single cutoff but documents the consequences of their inconsistency, functioning as a warning, while the commentary supplies the conceptual framework for understanding that warning. Both papers, read together, point toward the same next steps. For the millions of health care workers who quietly doubt their own competence, the practical takeaway is unchanged: the feelings are real, common, and linked to burnout. But for researchers hoping to put a single trustworthy number on how many people experience them, the message from this exchange is clear: the field first needs to agree on where the line should be drawn, and then prove that the line means something.

Subject of Research: Methodological debate over estimating prevalence rates of the Impostor Phenomenon using the Clance Impostor Phenomenon Scale

Article Title: Response to: Matters Arising from Salari et al. (2025): Why it is (currently) impossible to estimate accurate prevalence rates of the Impostor Phenomenon

Article References: Salari, N., Hashemian, S. H., Hosseinian-Far, A., Fallahi, A., Heidarian, P., Rasoulpoor, S., & Mohammadi, M. (2026). Response to: Matters Arising from Salari et al. (2025): Why it is (currently) impossible to estimate accurate prevalence rates of the Impostor Phenomenon. BMC Psychology, 14(1), Article 1400. https://doi.org/10.1186/s40359-026-05555-6

Image Credits: AI Generated

DOI: 10.1186/s40359-026-05555-6

Keywords: Impostor Phenomenon, prevalence, meta-analysis, Clance Impostor Phenomenon Scale, CIPS cutoffs, heterogeneity, psychological measurement, health service providers, burnout, screening thresholds, BMC Psychology, validation studies