Sasaki Jun

This study investigated the status of learning statistical inference based on the new high school curriculum guidelines, focusing on problems related to the standard normal distribution to assess understanding. Furthermore, based on past incorrect answer data for the standard normal distribution, effective teaching methods were proposed, implemented in educational practice, and their effectiveness was verified. Regarding problems on the standard normal distribution, having students draw graphs to understand the relationship with the formula and then review their work led to an increase in the correct answer rate. However, a significant number of errors persisted, including those based solely on rote understanding and careless mistakes. Measures to address careless mistakes still have room for improvement.

In this study, with the aim of effectively providing education in mathematics, data science, and AI to university and technical college students and working adults, we analyzed the understanding of the basic concepts of “descriptive statistics” necessary for such education and the points where students have difficulties, using a basic skills test administered to liberal arts university students. The results of the analysis showed that the students had a good level of understanding of questions that required them to find the mean, median and mode. However, the correct response rate for questions about variance and quartiles was low, and there were some students whose definitions were unclear. Therefore, I think that education on descriptive statistics should include guidance on the meaning of variance and quartiles.