Knowledge dimensions in hypothesis test problems

Abstract

The reformation in statistics education over the past two decades has predominantly shifted the focus of statistical teaching and learning from procedural understanding to conceptual understanding. The emphasis of procedural understanding is on the formulas and calculation procedures. Meanwhile, conceptual understanding emphasizes students knowing why they are using a particular formula or executing a specific procedure. In addition, the Revised Bloom's Taxonomy offers a twodimensional framework to describe learning objectives comprising of the six revised cognition levels of original Bloom's taxonomy and four knowledge dimensions. Depending on the level of complexities, the four knowledge dimensions essentially distinguish basic understanding from the more connected understanding. This study identifiesthe factual, procedural and conceptual knowledgedimensions in hypothesis test problems. Hypothesis test being an important tool in making inferences about a population from sample informationis taught in many introductory statistics courses. However, researchers find that students in these courses still have difficulty in understanding the underlying concepts of hypothesis test. Past studies also show that even though students can perform the hypothesis testing procedure, they may not understand the rationale of executing these steps or know how to apply them in novel contexts. Besides knowing the procedural steps in conducting a hypothesis test, students must have fundamental statistical knowledge and deep understanding of the underlying inferential concepts such as sampling distribution and central limit theorem. By identifying the knowledge dimensions of hypothesis test problems in this study, suitable instructional and assessment strategies can be developed in future to enhance students' learning of hypothesis test as a valuable inferential tool.