The law of small numbers bias is a representativeness heuristic that often leads individuals to draw extensive conclusions from small samples while underestimating the generalizability in larger ones. This study investigated whether individuals overestimate perceived discrimination in small employment samples and underestimate it in large ones. A pre-registered scenario-based experiment was conducted, where participants (N = 874) estimated probability of discrimination versus chance in skewed hiring distributions. We manipulated employment sample size (filling four vs. 100 positions) and ethnic majority (hiring more immigrants or Swedes) using a 2x2 between-subject design. A tendency for people to overestimate perceived discrimination by underestimating the impact of chance in small employment samples was revealed. Conversely, in large employment samples, people tended to underestimate perceived discrimination by overestimating the impact of chance. Hence, results aligned with the law of small numbers. Furthermore, participants were more inclined to attribute an event as discriminatory when organizations hired more Swedes than immigrants, reflecting (accurate) prototypes of discrimination. This study's implications are discussed concerning the repercussions of underestimating and overestimating perceived discrimination in hiring situations. Future research suggestions are also provided.

Thesis presents recent relevant studies of cognitive biases and false perceiving of randomness. There is an ongoing general belief that past performance automatically predicts future performance even in the cases when the result is act of randomness. This became inspiration for the main topic of my scientific research. Based on recent relevant literature I study if it is possible to influence people buying valueless information by evoking feeling of loss. Also I am trying to answer question which personal factors stand behind this behaviour. The results shows, that the manipulation with loss had nothing to do with buying valueless information. However it seems that personality factors are connected with this phenomenon to a certain extent. I had identified one factor called 'Irrational Thinking', which partly explains why 71% of the participants were interested in valueless transaction.

This thesis examines the response of a representative agent investor to sequences (streaks) of quarterly earnings surprises over a period of twelve quarters using the United States S&P500 constituent companies sample frame in the years 1991 to 2006. This examination follows the predictive performance of the representative agent model of Rabin (2002b) [Inference by believers in the law of small numbers. The Quarterly Journal of Economics. 117(3).p.775 816] and Barberis, Shleifer, and Vishny (1998) [A model of investor sentiment. Journal of Financial Economics. 49. p.307 343] for an investor who might be under the influence of the law of small numbers, or another closely related cognitive bias known as the gambler s fallacy. Chapters 4 and 5 present two related empirical studies on this broad theme. In chapter 4, for successive sequences of annualised quarterly earnings changes over a twelve-quarter horizon of quarterly earnings increases or falls, I ask whether the models can capture the likelihood of reversion. Secondly, I ask, what is the representative investor s response to observed sequences of quarterly earnings changes for my S&P500 constituent sample companies? I find a far greater frequency of extreme persistent quarterly earnings rises (of nine quarters and more) than falls and hence a more muted reaction to their occurrence from the market. Extreme cases of persistent quarterly earnings falls are far less common than extreme rises and are more salient in their impact on stock prices. I find evidence suggesting that information discreteness; that is the frequency with which small information about stock value filters into the market is one of the factors that foment earnings momentum in stocks. However, information discreteness does not subsume the impact of sequences of annualised quarterly earnings changes, or earnings streakiness as a strong candidate that drives earnings momentum in stock returns in my S&P500 constituent stock sample. Therefore, earnings streakiness and informational discreteness appear to have separate and additive effects in driving momentum in stock price. In chapter 5, the case for the informativeness of the streaks of earnings surprises is further strengthened. This is done by examining the explanatory power of streaks of earnings surprises in a shorter horizon of three days around the period when the effect of the nature of earnings news is most intense in the stock market. Even in shorter windows, investors in S&P500 companies seem to be influenced by the lengthening of negative and positive streaks of earnings surprises over the twelve quarters of quarterly earnings announcement I study here. This further supports my thesis that investors underreact to sequences of changes in their expectations about stock returns. This impact is further strengthened by high information uncertainties in streaks of positive earnings surprise. However, earnings streakiness is one discrete and separable element in the resolution of uncertainty around equity value for S&P 500 constituent companies. Most of the proxies for earnings surprise show this behaviour especially when market capitalisation, age and cash flow act as proxies of information uncertainty. The influence of the gambler s fallacy on the representative investor in the presence of information uncertainty becomes more pronounced when I examine increasing lengths of streaks of earnings surprises. The presence of post earnings announcement drift in my large capitalised S&P500 constituents sample firms confirms earnings momentum to be a pervasive phenomenon which cuts across different tiers of the stock markets including highly liquid stocks, followed by many analysts, which most large funds would hold.