id: 06458035
dt: j
an: 2015e.00234
au: Hoekstra, Rink
ti: Risk as an explanatory factor for researchersâ€™ inferential
interpretations.
so: Math. Enthus. 12, No. 1-3, 103-112 (2015).
py: 2015
pu: Information Age Publishing (IAP), Charlotte, NC; University of Montana,
Department of Mathematical Sciences, Missoula, MT
la: EN
cc: D20 K70
ut: research; questionable practices; reasoning with uncertainty; risk;
inferential statistics
ci:
li: http://www.math.umt.edu/tmme/vol12no1thru3/11_Hoekstra.pdf
ab: Summary: Logical reasoning is crucial in science, but we know that this is
not something that humans are innately good at. It becomes even harder
to reason logically about data when there is uncertainty, because there
is always a chance of being wrong. Dealing with uncertainty is
inevitable, for example, in situations in which the evaluation of
sample outcomes with respect to some population is required.
Inferential statistics is a structured way of reasoning rationally
about such data. One could therefore expect that using well-known
statistical techniques protects its users against misinterpretations
regarding uncertainty. Unfortunately, this does not seem to be the
case. Researchers often pretend to be too certain about the presence or
absence of an effect, and data are analysed in a selective way, which
impacts the validity of conclusions that can be drawn from the
techniques that are used. In this paper, the concept of risk is used to
explain why unwanted behaviour may not be as unreasonable as it eems,
once the risks that researchers face are taken into account.
rv: