Recent theoretical research has shown that the assumptionsthat both laypeople and researchers make about randomsequences can be erroneous. One strand of research showedthat the probability of non-occurrence of streaks of repeatedoutcomes (e.g., HHHHHH) is much higher than that for amore irregular sequence (e.g., HTTHTH) in short series ofcoin flips. This tallies with human judgments of theirlikelihood of occurrence, which have conventionally beencharacterized as inaccurate and heuristic-driven. Anotherstrand of research has shown that patterns of hits and missesin games like basketball, traditionally seen as evidence for theabsence of a hot-hand effect, actually support the presence ofthe effect. I argue that a useful way of conceptualizing thesetwo distinct phenomena is in terms of the distribution ofdifferent sequences of outcomes over time: Specifically, thatstreaks of a repeated outcome cluster whereas less regularpatterns are more evenly distributed.

Witness credibility is important for establishing testimonial value.The story model posits that people construct narratives fromevidence but does not explain how credibility is assessed. Formalapproaches use Bayesian networks (BN) to represent legalevidence. Recent empirical work suggests people might alsoreason using qualitative causal networks. In two studies,participants read a realistic trial transcript and judge guilt andwitness credibility. Study 1 varied testimonial consistency anddefendant character. Guilt and credibility assessments wereaffected by consistency but not prior convictions. Study 2constructed a BN to represent consistency issues. Individualparameter estimates were elicited for the corresponding BN tocompute posterior predictions for guilt and credibility. The BNprovided a good model for overall and individual guilt andcredibility ratings. These results suggest people construct causalmodels of the evidence and consider witness credibility. The BNapproach is a promising direction for future research in legalreasoning.