 About the Author Daniel J. Denis, Ph.D has served as Professor of Quantitative Psychology at the University of Montana since 2004 where he has taught numerous courses in advanced statistics to students in the social and natural sciences. He has also served as reviewer for journals such as Psychological Methods, BMC Medicine, International Statistical Review, Journal of Modern and Applied Statistical Methods, European Journal of Research Methods in Behavioral and Social Sciences, and Statistical Science of the Institute of Mathematical Statistics, among others. He has consulted in a variety of fields such as legal, medical, and social science more generally and has served as Expert Witness to the District Court of the United States. His teaching philosophy is to help and challenge students to think on their own so that they become independent, critical consumers and producers of knowledge rather than blindly following the status quo. Good science is about challenging the status quo, not blindly submitting to it or uncritically trusting authority. The purpose of education should be to learn how to think analytically, critically and ethically, not simply memorize and regurgitate material found in textbooks. His research interests, both academic and professional, include the dissemination and teaching of applied behavioral statistics and experimental design through the use of analytical, practical, philosophical, and historical insights, emphasizing foundations and how these tools should be effectively and ethically used in the evaluation of scientific findings. His goal is to help students see through the verbiage and marketing of quantitative methods to learn what is really there underlying the "glitter". What is machine learning? How exactly does a machine "learn"? What is really going on at a more foundational technical level? Expertise in an area implies seeing through the presumed complexity of what is advertised and marketed to get to the simple ingredients and concepts that make the methodology "tick" and are at their very foundation; cut through the smokescreen and see what's really going on behind the fancy words. Advanced procedures are often misunderstood due to a lack of understanding and appreciation for such foundations. If you are a prospective student interested in working with Dr. Denis in the Experimental Psychology Program at the University of Montana, or would like to receive educational mentorship, please contact him directly at daniel.denis@umontana.edu or via his faculty page at the University of Montana.
Book
Publishing
& Recent
Directions
The
historical
evolution of
statistical progress
occurs in the
context of a
wider
zeitgeist, and
conceptual
seedlings
predate
rigorous
definition by
sometimes
thousands of
years. This
has been well
established by
historians of
statistics and
science.
Understanding
how
quantitative
methods
evolved as a
way of mapping
the "real
world" or
whether they
evolved
independent of
practical
considerations
is an interest
of mine, as
well as what
can vs. cannot
be ethically
concluded from
a scientific
investigation
that employs
such
quantitative
tools. Too
often,
scientists
overestimate
the power of
the analytical
tool in
supporting
their
scientific
hypotheses. In
my teaching, I
aim to help
students
understand and
appreciate
just what can
vs. cannot be
concluded from
the use of
statistics in
a research
article. In
this sense, my
teaching
encourages
students to
think independently and critically when
confronted
with science
that uses
statistics,
and to look at
the "Big
Picture" when
interpreting a
research
article.
Assumptions,
methodology,
design,
statistics,
psychometric
properties of
variables,
these are all
factors that
need to be
considered to
arrive at the
"bottom line"
of what a
research
report is
communicating,
which does not
always
coincide with
what authors
would like you
to take away
from the
research.
Critically
evaluating
research does
not imply
being
"negative," or
"pessimistic,"
it simply
means trying
to accurately
assess what
can vs. cannot
be ethically
gleamed from a
research
finding.
Design, not
statistical
analysis, is
usually what
is most
important in
establishing a
scientific
finding. Complex
statistical
analyses on an
unstable
design
foundation is
analogous to
building a
house on
quicksand. The
house may
be impressive,
but the entire
structure will
fail
inspection.
Understanding how statistical models work and function is vital to understanding research that is driven by statistical methods. Understanding the fundamental statistical and philosophical foundations that precede the application of quantitative tools is paramount for understanding how the statistical methodologies are being applied in a given situation, and whether that application is valid and ethical. Mathematical statisticians (bless them!) and others have provided science with exceptional tools, and it is essential that students learn how to successfully (and ethically) incorporate that wealth of mathematics into their scientific endeavors and applications. Too often, this incorporation is done recklessly with insufficient knowledge of what the statistical method can vs. cannot tell you about your data.
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For
the better
part of the
last 10
years (since
2012 when I
received my
first book
contract with
Wiley), I have
been authoring
books on
applied
statistics for
the social and
natural
sciences
featuring R,
SPSS, and
Python
software. The
most
significant
and thorough
(and the one
I'm most proud
of and which
took an
inordinate
(mildly put)
amount of time
and effort) of
these projects
is 
