This research explored students' trust in ChatGPT, in answering challenging trivia questions on an online multiple-choice questionnaire. Despite extensive research on ChatGPT's capabilities, a...Show moreThis research explored students' trust in ChatGPT, in answering challenging trivia questions on an online multiple-choice questionnaire. Despite extensive research on ChatGPT's capabilities, a significant gap in the literature remains regarding human trust in this AI system, particularly in educational contexts. This study aimed to assess to what extent do university students trust this novel AI, uncovering biases towards or against AI-generated responses. Using a between-subjects design, we manipulated answer source transparency and correctness. Each question presented participants with one human-generated and one AI-generated answer option. The results were analysed through paired samples and independent two-sample t-tests, alongside Ordinary Least Square (OLS) regression models. The findings revealed a distinct preference for human-generated content, which persisted even when answers were incorrect. This indicated a lower level of trust in ChatGPT among our participants, implying that they did not over-rely on AI-generated content. Surprisingly, the trend leaned towards an over-reliance on human-generated responses.Show less
The reliability of statistics is essential for official statistics. With administrative data more often used instead of survey data, non-sampling errors become important factors in the accuracy of...Show moreThe reliability of statistics is essential for official statistics. With administrative data more often used instead of survey data, non-sampling errors become important factors in the accuracy of statistics. For domain statistics, such as yearly turnover of enterprises, classification errors occur. This study aims to measure the effect of classification errors on domain statistics, more specially, bias and variance due to classification errors. In this study, a new method was developed that applies a Gaussian mixture model, estimated by the EM algorithm, in short referred to as the EM method. Further another method was introduced that combined the EM method with bootstrapping, referred to as the combined method. Among them, the EM method only estimates bias, and the combined method is able to estimate both bias and variance. Together with a previously used bootstrap method, the three methods were tested in a simulation study and in a case study. The bias and variance estimates from the three methods were compared with their corresponding true values in different settings. The results showed that the bias estimates from the EM and the combined method were closer to the true values compared to the bootstrap method; The combined method had closer outputs on variance estimation than the bootstrap method. The EM and the combined method were equally accurate in estimating the true bias. These results suggest that the EM and the combined method estimated the bias and variance more accurately than the bootstrap method. In practice, the combined method is recommended since both the bias and the variance can be estimated. In a situation with a very large data set, where the variance is usually small and the bias is of most concern, the EM method may be preferred.Show less
Master thesis | Theology and Religious Studies (Master)
open access
The thesis looks into the usefulness of using the concept 'catholic bias' as opposed to 'protestant bias'. This was done by examining the Catholic background of two anthropologists. It argues that...Show moreThe thesis looks into the usefulness of using the concept 'catholic bias' as opposed to 'protestant bias'. This was done by examining the Catholic background of two anthropologists. It argues that the Catholic background had an important influence on their anthropological work, which makes the concept of 'Catholic bias' interesting for further research. It further argues a positive contribution of religious backgrounds, or bias, as a source of inspiration and unique understanding.Show less
