REASONING ABOUT INFORMAL STATISTICAL INFERENCE: ONE STATISTICIAN’S VIEW

By Allan Rossman. 2008

119 citations recorded by the source corpus.

This paper identifies key concepts and issues associated with the reasoning of informal statistical inference. I focus on key ideas of inference that I think all students should learn, including at secondary level as well as tertiary. I argue that a fundamental component of inference is to go beyond the data at hand, and I propose that statistical inference requires basing the inference on a probability model. I present several examples using randomization tests for connecting the randomness used in collecting data to the inference to be drawn. I also mention some related points from psychology and indicate some points of contention among statisticians, which I hope will clarify rather than obscure issues.

Subfields: History and Philosophy of Science, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/c7a59efb03d990164e183549e788062039edc229

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In History and Philosophy of Science

In Statistics and Probability

Citations in the corpus

Statistical Education in the 21st Century: A Review of Challenges, Teaching Innovations and Strategies for Reform (2012, background): “The extremely fast-developing technology offers numerous technological tools for statistics education.”
Towards more accessible conceptions of statistical inference (2011, background): “On the contrary, it is rather like looking at the world while standing on your head (for a discussion of student’s difficulties, see sections 5–7 of Rossman (2008) and Cobb and Moore (1997), section 3.5).”
THE CHALLENGE OF PREPARING PRESERVICE TEACHERS TO TEACH INFORMAL INFERENTIAL REASONING (2010, background): “) and made generalizations which extended beyond the data (Makar & Rubin, 2007; Pfannkuch, 2006; Rossman, 2008).”