2006•Unpublished venueRequires access

Exploring informal inference with interactive visualization software

Andee Rubin, James K. L. Hammerman, Cliff Konold

Open publisher page 56 citations

Abstract

Most statistics educators would agree that statistical inference is both the central objective of statistical reasoning and one of the most difficult ideas for students to understand. In traditional approaches, statistical inference is introduced as a quantitative problem, usually of figuring out the probability of obtaining an observed result on the assumption that the null hypothesis is true. In this article, we lay out an alternative approach towards teaching statistical inference that we are calling “informal inference. ” We begin by describing informal inference and then illustrate ways we have been trying to develop the component ideas of informal inference in a recent data analysis seminar with teachers; our particular emphasis in this article is on the ways in which teachers used TinkerPlots, a statistical visualization tool. After describing teachers ’ approaches to an inferential task, we offer some preliminary hypotheses about the conceptual issues that arose for them.

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What this paper is about

Most statistics educators would agree that statistical inference is both the central objective of statistical reasoning and one of the most difficult ideas for students to understand. In traditional approaches, statistical inference is introduced as a quantitative problem, usually of figuring out the probability of obtaining an observed result on the assumption that the null hypothesis is true. In this article, we lay out an alternative approach towards teaching statistical inference that we are calling “informal inference. ” We begin by describing informal inference and then illustrate ways we have been trying to develop the component ideas of informal inference in a recent data analysis seminar with teachers; our particular emphasis in this article is on the ways in which teachers used TinkerPlots, a statistical visualization tool. After describing teachers ’ approaches to an inferential task, we offer some preliminary hypotheses about the conceptual issues that arose for them.

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OpenAlex reports 56 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

Most statistics educators would agree that statistical inference is both the central objective of statistical reasoning and one of the most difficult ideas for students to understand. In traditional approaches, statistical inference is introduced as a quantitative problem, usually of figuring out the probability of obtaining an observed result on the assumption that the null hypothesis is true. In this article, we lay out an alternative approach towards teaching statistical inference that we are calling “informal inference. ” We begin by describing informal inference and then illustrate ways we have been trying to develop the component ideas of informal inference in a recent data analysis seminar with teachers; our particular emphasis in this article is on the ways in which teachers used TinkerPlots, a statistical visualization tool. After describing teachers ’ approaches to an inferential task, we offer some preliminary hypotheses about the conceptual issues that arose for them.

Key concepts: Inference, Statistical inference, Computer science, Fiducial inference, Visualization, Predictive inference, Frequentist inference, Data science

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