An Overview of Students’ Learning Problems in Hypothesis Testing

Krishnan, Saras; Idris, Norisma

doi:10.17576/jpen-2015-4002-12

Cited by 7 publications

(9 citation statements)

References 4 publications

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“…Incorrect identification of the test statistic formula and incorrect computation of the calculated value of the test statistic were moderately-frequent mistakes. These results are similar to the typology identified by Link (2002) and Krishnan and Idris (2015). The most frequent mistakes in testing for a single population mean were found to be the incorrect taking of the decision and drawing of the conclusion, followed by incorrect formulation of H 0 and H 1 , incorrect identification of one/two tailed test, and incorrect identification of the critical value.…”

Section: Resultssupporting

confidence: 86%

“…she suggested that statistical instruction should focus more on the development of student understanding of overall statistical hypothesis testing, particularly on the development of understanding of the logic of indirect reasoning and its role in statistical hypothesis testing, along with uncertainty and variability. Krishnan and Idris (2015) provided an overview of the problems faced by students in the learning of hypothesis testing. They suggested a classification of twenty-one distinct types of mistakes in hypothesis testing under six broad groups, viz.…”

Section: Introductionmentioning

confidence: 99%

“…The literature reviewed above has highlighted the importance of the problem of students' and teachers' understanding of hypothesis testing, and the need for appropriate pedagogical approaches to foster a clear understanding of the probability and sampling theoretic concepts underlying hypothesis testing. The present study examines students' mistakes in applying hypothesis testing, as in link (2002) and Krishnan and Idris (2015), in order to identify the causes of different types of mistakes made in hypothesis testing, in order to suggest pedagogical strategies to avoid these mistakes.…”

Section: Introductionmentioning

confidence: 99%

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Pedagogical Issues in Hypothesis Testing (Isu-isu Pedagogi dalam Pengujian Hipotesis)

Dash

2018

JPEN

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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.

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Section: Resultssupporting

confidence: 86%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Pedagogical Issues in Hypothesis Testing (Isu-isu Pedagogi dalam Pengujian Hipotesis)

Dash

2018

JPEN

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“…While hypothesis testing is an important tool of statistical inference (Krishnan & Idris, 2015), its use to interpret experimental data has received criticism (Motulsky, 2014;Nickerson, 2000;Nuzzo, 2014) due to the common misunderstandings by both scientists and students when using this method (Batanero, 2000;Dolor & Noll, 2015;Vallecillos, 2000), such as the meaning behind the p-value. Rather than abandon this method of inference entirely, researchers have called for improving the education and understanding of hypothesis testing.…”

Section: Introductionmentioning

confidence: 99%

Students’ understanding of the concepts involved in one-sample hypothesis testing

Stalvey

Burns-Childers

Chamberlain

et al. 2019

The Journal of Mathematical Behavior

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Hypothesis testing is a prevalent method of inference used to test a claim about a population parameter based on sample data, and it is a central concept in many introductory statistics courses. At the same time, the use of hypothesis testing to interpret experimental data has raised concerns due to common misunderstandings by both scientists and students. With statistics education reform on the rise, as well as an increasing number of students enrolling in introductory statistics courses each year, there is a need for research to investigate students' understanding of hypothesis testing. In this study we used APOS Theory to investigate twelve introductory statistics students' reasoning about one-sample population hypothesis testing while working two real-world problems. Data were analyzed and compared against a preliminary genetic decomposition, which is a conjecture for how an individual might construct an understanding of a concept. This report presents examples of Actions, Processes, and Objects in the context of one-sample hypothesis testing as exhibited through students' reasoning. Our results suggest that the concepts involved in hypothesis testing are related through the construction of higher-order, coordinated Processes operating on Objects. As a result of our data analysis, we propose refinements to our genetic decomposition and offer suggestions for instruction of one-sample population hypothesis testing. We conclude with appendices containing a comprehensive revised genetic decomposition along with a set of guided questions that are designed to help students make the constructions called for by the genetic decomposition.

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“…Students also did not understand the purpose of the problem (Diniyah, Akbar, Akbar, Nurjaman, & Bernard, 2018). Furthermore, Link (Krishnan & Idris, 2015) said students have trouble when stating the hypotheses. Students did not understand the definitions of hypotheses (Sotos, Vanhoof, Noortgate, & Onghena, 2009).…”

Section: Idiosyncratic Reasoningmentioning

confidence: 99%

Students’ Statistical Reasoning in Statistics Method Course

Rohana

Ningsih

2019

JPM Unsri

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The role of statistics is wide and crucial in daily life, making statistics important. Many students have difficulty understanding statistics. This study aims to determine students' statistical reasoning about inference statistics, which is limited to the subject matter of the testing hypotheses about two-sample hypotheses testing. This study used descriptive research method. The subjects were 25 students of third-year Mathematics Education Departement at Universitas PGRI Palembang in the academic year 2018/2019. Data were collected through tests and interviews. Data were analyzed through descriptive quantitative. The results of data analysis showed that 32% of students had level 1 statistical reasoning (the lowest level), 20% were at level 2, 28% at level 3, 12% at level 4 and 8% at level 5 (highest level). Based on the result, it can conclude that students' statistical reasoning ability in learning statistical method is not satisfactory, students are still very lacking in reasoning.

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An Overview of Students’ Learning Problems in Hypothesis Testing

Abstract: Hypothesis testing is an important tool of statistical inference and is taught in most introductory

Cited by 7 publications

References 4 publications

Pedagogical Issues in Hypothesis Testing (Isu-isu Pedagogi dalam Pengujian Hipotesis)

Pedagogical Issues in Hypothesis Testing (Isu-isu Pedagogi dalam Pengujian Hipotesis)

Students’ understanding of the concepts involved in one-sample hypothesis testing

Students’ Statistical Reasoning in Statistics Method Course

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