Why is it difficult to understand statistical inference? Reflections on the opposing directions of construction and application of inference framework

Kula, Fulya; Koçer, Rüya Gökhan

doi:10.1093/teamat/hrz014

Cited by 9 publications

(8 citation statements)

References 30 publications

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“…This implies that we cannot reject our guess about the population parameter. And, (ii) it is safer to infer that a result with such a low probability would not really have happened by chance and thus, the sample result does not belong to the population with our guessed parameter value why we reject the guess (Kula & Koçer, 2020). The focus of the present study is on informal hypothesis testing (IHT).…”

Section: Introductionmentioning

confidence: 94%

Students’ Informal Hypothesis Testing in a Probability Context With Concrete Random Generators

Nilsson

2020

SERJ

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This study examines informal hypothesis testing in the context of drawing inferences of underlying probability distributions. Through a small-scale teaching experiment of three lessons, the study explores how fifth-grade students distinguish a non-uniform probability distribution from uniform probability distributions in a data-rich learning environment, and what role processes of data production play in their investigations. The study outlines aspects of students’ informal understanding of hypothesis testing. It shows how students with no formal education can follow the logic that a small difference in samples can be the effect of randomness, while a large difference implies a real difference in the underlying process. The students distinguish the mode and the size of differences in frequencies as signals in data and used these signals to give data-based reasons in processes of informal hypothesis testing. The study also highlights the role of data production and points to a need for further research on the role of data production in an informal approach to the teaching and learning of statistical inference. First published December 2020 at Statistics Education Research Journal: Archives

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Section: Introductionmentioning

confidence: 94%

Students’ Informal Hypothesis Testing in a Probability Context With Concrete Random Generators

Nilsson

2020

SERJ

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“…"Now we move on to the second question (Question 2.a.i). The heights, in centimeter, of 8 In answering question 2a(i), student S61 did not rearrange the ungrouped data before looking for the value of Q3 and Q1. Student S61 was lack of the awareness of the importance of order of the data set in finding interquartile range.…”

Section: Interview Transcript 20 (S61)mentioning

confidence: 99%

“…Although many statistics classrooms in the world nowadays are experiencing the integration of technology and practicing creative instructions to teaching statistics, past research show that students have difficulties related to the learning of statistics in measures of central tendency, variability and distribution, and statistical reasoning [5]- [8]. Teachers and researchers of statistics often overestimate school students' mastery of fundamental concepts in statistics and underestimate their difficulties in the same subject [9].…”

Section: Introductionmentioning

confidence: 99%

Making sense of students’ errors in solving problems related to measures of dispersion

Leng

Meng²

2023

IJERE

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The importance of statistical learning has been widely discussed. However, little effort has been made to understand the difficulties of students in learning measure of dispersion, which is the key component to statistical learning. The present study sought to identify the reasons behind the errors committed in solving problems related to measures of dispersion, by examining students’ errors in the diagnostics tests, followed by in depth interviews to elicit their thinking and understanding. There were 85 grade-11 high school students involved in the first phase of the quantitative research and 10 students with weak performance were subsequently selected for the second phase qualitative research. The interviews were conducted using the contingent teaching model. The findings indicated that students’ committing errors in solving problems related to measure of dispersion due to lacking statistical vocabulary knowledge, weak symbol sense, rote learning, low statistical reasoning and statistical thinking ability. The results of the study and proper remedials are discussed.

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“…The most pronounced differences between both groups were in Episode 3 and Episode 7. The former introduced participants to one of the most challenging conceptual topics in introductory statistics, inference statistics (Kula & Koçer, 2020). The latter focused on the applicable of fundamental statistics concepts in understanding a statistical analysis test.…”

Section: Educational Implicationsmentioning

confidence: 99%

A technology-enhanced learning intervention for statistics in higher education using bite-sized video-based learning and precision teaching

Tan¹,

Davies²,

Nicolson³

et al. 2022

RPTEL

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Adjustments to life and learning following the COVID-19 pandemic have transformed user acceptance of online learning methods. It is, therefore, imperative to analyse factors relating to user performance and preferences for such interactions. In this study, we combined video-based learning with precision teaching to reinforce previously learnt statistics skills in university students without a mathematical background. We developed a learning design consisting of eight ‘bite-sized’ online learning episodes. Each episode started with a brief learning video followed by a practice phase and an end-of-episode assessment. The practice phase differed in two groups of participants, matched on statistics attainment pre- intervention. A precision-teaching intervention group (N = 19) completed practice guided by a frequency-based approach aiming at building fluency in statistics. A control group (N = 19) completed self-directed practice for the same amount of time as the intervention group. All participants completed a statistics attainment test and a questionnaire on their attitudes towards statistics pre- and post- intervention, and a review of the learning materials post-intervention. The intervention group achieved, consistently, higher scores in all end-of-episode assessments compared to the control group. Both groups showed significant and comparable improvements in statistics attainment post-intervention. Both groups also reported more positive feelings towards statistics post-intervention, while the review of the learning materials suggested that the video-based learning design was well-received by students. Our results suggest that video-based learning has great potential to support, as a supplementary teaching aid, university students in learning statistics. We discuss future research directions and implications of the study.

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Why is it difficult to understand statistical inference? Reflections on the opposing directions of construction and application of inference framework

Cited by 9 publications

References 30 publications

Students’ Informal Hypothesis Testing in a Probability Context With Concrete Random Generators

Students’ Informal Hypothesis Testing in a Probability Context With Concrete Random Generators

Making sense of students’ errors in solving problems related to measures of dispersion

A technology-enhanced learning intervention for statistics in higher education using bite-sized video-based learning and precision teaching

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