Optimism is inherently dangerous in some cases, approaching a near “madness of insisting that all is well when we are miserable” (Voltaire).2 Bayesian analysis snowballs scientific evidence, and is currently new (to me). Ostensibly unrelated, these concepts can combine as a charming yet consequential cocktail of expectations and sense-making.
For example, as a “Die Hard” optimist,3 I should have a few advantages measured in psychological health (e.g., eliminating or reducing stressors and negative emotions; Nes & Segerstrom, 2006),4 and physical health (e.g., survival and cardiovascular outcomes; Rasmussen, Scheier, & Greenhouse, 2009).5 However, recent evidence from my field suggests that state-based or 'little' optimism—not trait-based or 'big' optimism—gives the competitive edge for job-related outcomes (Kluemper, Little, & DeGroot, 2009),6 and state-based optimism is rooted in domain-specific experience.
This is where Bayes comes into the picture. Bayesian analysis is a statistical approach that allows us to use informative priors7 to update our knowledge about the specific likelihood of a particular outcome. Hence, unlike the run of the mill null hypothesis testing typically found in our field (i.e., the statistical approach that can only tell us if we’re likely to have obtained a particular result, assuming the null is true), Bayes estimates the actual likelihood of a particular result and provides a more accurate estimate by integrating previous information—it’s like a mini meta-analysis every time!8
So, coming full circle (and returning to my opening quote), being fooled once can be written off as a mistake or a misunderstanding—these things happen to the best of us, right? Being fooled twice, well, that could still be overlooked by a trait optimist or perhaps even attributed to circumstance (it ALWAYS comes back to context in my field of research). But after being fooled three times, a null hypothesis tester and eternal optimist might return to the former (false) explanation, whereas a Bayesian includes the two informative priors, deciding instead that duplicity is highly probable. At this point, even big, trait optimists must carefully incorporate a little, state optimism to realize that it’s time to move on or risk becoming the fool.
After all, it's live free or die hard...right Bruce?