## 4 Study Results
### Hypothesis Testing  
The data collected show that in most cases, the outputs precisely matched expectations. In some instances, the findings from the data revealed truths that could not be seen directly. Some cases were surprising and went beyond any logical explanation. We observed that the dataset is heterogeneous, and simple generalisation could distort the results. The study avoids giving universal attributes to the whole sample of investor respondents. Instead, significant biases in profiled groups were measured in selected characteristics such as gender, age, investment amount, and years of experience on the stock market. Not all attributes are significant in all cases. We used a \(t\)-test for equality of means. For pair comparisons, we applied the standard version of the test, while for multiple comparisons, we used the Bonferroni correction. Firstly, we compared the results in groups by gender by testing the differences in biases between male and female investors (Table 1). The only meaningful differences in biases observed between the two genders were in the cases of gambler's fallacy and availability heuristics (where the \(p\)-value of the \(t\)-test for equality of group means is below the significance threshold of 0.05). We surveyed the investors about the availability cascade based on the information from the news. It was revealed that women are more likely to make a decision based on a mental shortcut that relies on immediate examples (such as a news report that mentions a high level of trading on specific days for the company they invested in), while men are less likely to fall into this bias. However, even if the aforementioned two effects were observed, it was found that gender does not play a role in clustering or building investors' profiles. Second, we determined **which bias is the strongest**. A notable finding was that the most common fallacy for both genders was confirmation bias. Many investors were not keen to verify the information gained prior to action and considered immediate action, \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Female Mean** & **Male Mean** & **t Statistics** & \(p\)**-Value** \\ \hline Confirmation & 4.133 & 3.721 & 0.867 & 0.364 \\ \hline Loss aversion & 3.594 & 3.131 & \(-\)0.992 & 0.341 \\ \hline Gambler’s & 3.647 & 3.406 & \(-\)8.435 & 0.000 * \\ \hline Availability & 3.112 & 2.861 & \(-\)5.967 & 0.000 * \\ \hline Hot hand’s & 3.058 & 3.091 & \(-\)0.481 & 0.440 \\ \hline Bandwagon & 3.295 & 2.898 & 1.065 & 0.329 \\ \hline Dunning-Kruger & 3.459 & 3.282 & \(-\)3.077 & 0.054 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Fallacies based on gender. even if said information could turn out to be false. The investors fell into availability bias in the same way. Both availability and confirmation bias serve as evidence that the information to which investors have been exposed greatly affects the way they respond in the stock market (even though all investors need reliable information to continue to make stable investments). Conversely, it demonstrates how dangerous it is to surround investors with targeted information, which may lead to designed and planned actions which favour certain agencies and companies. The existence of these biases means that news, advertisements, and the media all have a considerable influence on the way investors make decisions. Third, we explicitly asked investors about their **dedicated approaches to succeeding in TSE** in order to better understand these methods and confirm these patterns in the direct answers to the question: \"Which approach do you use in your stock analysis?\". It is reasonable to expect that results would differ from those found in the experiments we had already conducted. The possible choices were as follows: Technical, Fundamental, News, Consultation, Self-Strategy and No Strategy; more than one choice was allowed.