Engineers working in manufacturing will usually perform a routine statistical analysis to evaluate the performance of a production line. However, this article argues that such routine statistical analysis will only focus on conformity and not on developing problem -solving skills. Problem - solving skills involving statistics would lead to not only higher quality control during manufacturing processes, but also efficient problem diagnostics using manufacturing data. To encourage learning and gaining a deeper understanding of statistics, this article describes the application of raincloud plots and test of normality via a simulated case study. All analysis described in the simulated case study can be performed freely v ia open -source software and without programming knowledge. KEYWORDS : Raincloud plot; statistics; production line; engineering; case study. 1.0 INT RODUCTIO N Engineering and statistics are two inseparable fields. Engineering students will learn general statistics courses which include basic descriptive statistics and hypothesis testing [1, 2] . However, such statistical courses might be inadequate for real -life examples. One example of a limitation in teaching and performing engineering activities is about handling non -normal distribution data. Commonly, engineers in any field will be able to p erform routine statistical analysis for the system in their respective fields. The routine statistical analysis usually includes descriptive statistics and variation of statistical control process practices. However, it is undeniable that such routine stat istical analyses were analytical strategies based on predecessors and only focused on the conformity of the production line. An engineer must be equipped with more advanced statistical knowledge than the routine statistical analysis strategy in order to so lve a problem that might occur 2 outside the boundary of routine statistical analysis output. One example is non -normal distribution data. Non -normal distribution occurs much more than normal distribution in actual engineering practices [3] One common strategy is to transform the non -normal data into a normal distribution with a variety of transformation strategies. However, does such a strategy teach the engineer about the system or this strategy had become an extended version of routine st atistical analysis? This does not mean that the data transformation strategy is incorrect, but data transformation needs a deeper understanding of transformation strategy for correct execution [4]. Data transformation had been discouraged since it can lead to wrong interpretation of the actual data if precaution steps are not taken [5, 6] . The article intends to stress the importance of acquiring a deeper understanding of statistics outside the normal general introduction to statistics syllabus. There are anecdotes regarding engineering problems that had caused a detrimental loss in terms of human effort, resources and money due to the reason that the analyst does not realize that the data is non -normal in the first place and apply incorrect analysis to guide decision making [3]. Taking this into account, this article aims to showcase raincloud plots, a data visualization plot with minimal statistical interferences. Raincloud plot was developed by researchers in the field of neuroscience for the purposes of data transparency amon g researchers [7]. Although it is mainly utilized in basic science research, raincloud plots had also been utilized in applied science research. Examples of applied science research include detecting tempe sensory p