This was a statistic heralded by President-elect Donald Trump (Appelbaum, 2016, para. 2), in a speech during the election campaign, to illustrate the apparently huge number of unemployed Americans and, thus, to expose the perilous state of the American economy. However, if considered critically, this is also a statistic that is incredibly misleading. Trump may be correct that fewer Americans, as a percentage of the total population, are engaged in traditional employment today compared to previous decades. However, the statistic above is not proof that more Americans are unemployed and, indeed, is more indicative of the fact that 20% of American households are headed by retirees (Jacobson, 2016). In this statistic, Trump is tacitly classifying retirees, 16-to-17-year-olds, and stayat-home parents as being within the ranks of the unemployed. Although this classification may be technically accurate, it misleads the public about the general state of the economy. The recent election campaign was characterized and arguably won on the basis of such bald misinformation and the mischaracterization of seemingly authoritative and objective statistics, figures, and facts. In a year dominated by the twin phenomena of fake news (Holan, 2016) and post-truth politics (Wang, 2016), it is more vital than ever to foster the general public's critical thinking about the numbers and statistics used-and abused-by business leaders, advocates, and policymakers. In this paper, I will describe an intervention designed to foster such critical thinking, and to enable the public to better distinguish between misleading and representative statistics. I will describe the development of this intervention, informed by our understanding of how people reason about statistics and numbers within the context of topics in the public domain, as well as techniques to foster critical thinking in the classroom. The Impact of Statistics A prevailing view in the realm of social psychology has been that of cultural cognition-the idea that people form risk perceptions and thus make decisions and form worldviews that cohere strongly with their cultural and political values (Kahan, Jenkins-Smith, & Braman, 2011). This theory is used to explain why certain groups do not believe in climate change or the effectiveness of vaccines, despite overwhelming scientific evidence to the contrary. This is because groups have a tendency to view empirical evidence in a biased manner, confirming evidence that fits with their beliefs at face value, while holding disconfirming evidence to higher critical standards (Lord, Ross