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Magnitude-based inference and its application in user research

P. Schaik,M. Weston

Published 2016 in Int. J. Hum. Comput. Stud.

ABSTRACT

Magnitude-based inference offers a theoretically justified and practically useful approach in any behavioural research that involves statistical inference. This approach supports two important types of inference: mechanistic inference and practical inference to support real-world decision-making. Therefore, this approach is especially suitable for user research. We present basic elements of magnitude-based inference and examples of its application in user research as well as its merits. Finally, we discuss other approaches to statistical inference and limitations of magnitude-based inference, and give recommendations on how to use this type of inference in user research. Magnitude-based inference is a useful alternative for analysing user-research data.Goal-setting in user research is supported by choosing a smallest important effect.The approach uses the smallest important effect in making an inference.As a consequence, a clear effect is never an artefact of sample size.Practical inference is supported by weighing harm and benefit appropriately.

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