Stars everywhere: Revealing the prevalence of star performers using empirical data published in entrepreneurship research

Scholars have long called for moving beyond a narrow focus on average performance toward a more direct investigation of the variance in performance. While a few studies have evaluated star entrepreneurs, most empirical research continues to focus on average performers. This lacuna has constrained not only the development of theories but also the accumulation of data on the distribution of performance. In response, this study uses simulations and heuristics to extract distributional information from descriptive statistics commonly reported in published research (i.e., mean, standard deviation, and sample size). Applying this approach to studies recently published in high-impact entrepreneurship journals shows that (a) the suggested methodology can provide rough estimates of the skew and shape of performance distributions, and (b) right-skewed, heavy-tailed distributions featuring star performers are ubiquitous in entrepreneurship, thus reinforcing calls for more direct studies of performance distributions in entrepreneurship. [Display omitted] •Most published studies in entrepreneurship myopically focus on average performance.•Heuristics can help ‘reverse engineer’ the likely shapes of performance distributions.•Simulations can help ‘reverse engineer’ the skewness of performance distributions.•Star performance is ubiquitous in entrepreneurial contexts, yet largely unstudied.•Entrepreneurship scholars should adopt a distributional perspective to study stars.