Pedagogical issues in hypothesis testing

• Main Author: Mihir Dash
• Format: Article
• Language: English
• Published: Penerbit Universiti Kebangsaan Malaysia 2018
• Online Access: http://journalarticle.ukm.my/12144/1/17850-76579-1-PB.pdf; http://journalarticle.ukm.my/12144/; http://ejournal.ukm.my/jpend/issue/view/1089
• Institution: Universiti Kebangsaan Malaysia

Hypothesis testing is a statistical technique which is used to evaluate assumptions about a population on the basis of sample data, to determine the extent to which they are tenable. Hypothesis testing is the most widely-applied statistical technique, particularly because of the emphasis on hypothesis development and testing in the scientific method. Unfortunately, students and researchers are quite prone to making mistakes and misinterpreting inferences in hypothesis testing. These mistakes and misinterpretations tend to arise from insufficient understanding of the probability and sampling theory underlying the logic of hypothesis testing. The present study attempts to identify the causes of different types of mistakes made in hypothesis testing, in order to suggest pedagogical strategies to avoid these mistakes. The data for the study was collected from a sample of postgraduate management students in Bangalore, India, using specially-designed business decision-making case lets based on hypothesis testing. The analysis focuses on the incidence of different types of mistakes that the respondents committed, particularly with respect to the type of tests, and uses multiple linear discriminant analysis to identify the factors impacting the overall inference, i.e. the correct taking of the decision and the correct drawing of the conclusion. The key finding of the study is that both the formulation and computation factors play a significant role in taking the overall inference. Further, in each panel, the critical discriminator was found to be the aspect for which the incidence of mistakes was highest. With increasing complexity of the hypothesis test, the computation factor was found to become more important. In panels A and B (tests for a single population mean and proportion, respectively), formulation aspects were found to be the most significant discriminators, and in panel C (test for equality of means), both formulation and computation aspects were significant; on the other hand, for the remaining panels (test for independence, one-way ANOVA, and two-way ANOVA), only computation aspects were significant. The study contributes to the literature by proposing some pedagogical strategies for teaching of different types of hypothesis tests based on the findings.