Comment on Probabilities of Clinical or
Practical Significance Alan M
Batterham Sportscience 6,
sportsci.org/jour/0201/amb.htm, 2002 (725 words) 
Will Hopkins' article provides a timely and
valuable elaboration of the short item in
the previous edition of Sportscience. Up to now, forward thinking researchers
have been able to access a spreadsheet at newstats.org to calculate likelihoods of an observed effect
being clinically or practically beneficial, trivial, or harmful (derived from
the t distribution). This novel approach has extended our battery of tools
for drawing inference from data, beyond both the outmoded tests against the
null hypothesis and more contemporary estimation methods using confidence
intervals or limits. The current article, with the associated table, provides
a tool for illuminating the probabilities derived from the spreadsheet. The
qualitative descriptors are an important enhancement, facilitating interpretive
statements in the discussion and conclusions sections of the dissemination of
research findings. I strongly encourage readers attempting to get to grips
with these concepts to read this article alongside the previous short item,
and to view the PowerPoint slide show. The benefits of a research design,
data analysis, and interpretation approach based on quantifying clinical
significance, rather than mere statistical significance, have been recognized
widely in the medical sciences. Unfortunately, the message has not been
widely adopted in the exercise science field. This article will hopefully
encourage more researchers in our field to adopt this approach, and challenge
some cherished assumptions and dogma. It will require a concerted effort by many
people to bring about this paradigm shift away from the comfort zone of
P<0.05. The
summary of advice provides a clear framework for reporting of research
findings. The fourth bullet point often presents the biggest challenge for
researchers and, indeed, clinicians and practitioners. Determining, in
advance of the study, the minimum clinically important difference (MCID), or
smallest worthwhile effect, is not a trivial issue. However, it is one that
must be tackled if any true insight is to be derived from the research.
Firstly, knowledge of the MCID, combined with a value for the ‘noise’ in the
measurement from a reliability study, permits the calculation of an
appropriate sample size for adequate precision of estimation. Secondly,
knowledge of the MCID allows for the calculation of the probabilities of
clinically important/ trivial/ or clinically harmful effect in the
population, with the associated qualitative descriptors from the table. I am
particularly moved by the last paragraph regarding the choice of an
appropriate confidence interval to convey precision. The most commonly used
are the 95% and 99% confidence intervals. These values are arbitrary and have
been widely adopted, primarily, due to their congruence with null hypothesis
testing at the 0.05 or 0.01 alpha levels. In other words, if the 95% or 99%
confidence interval does not contain the value assumed under the null or
zero, then P is <0.05 or <0.01, respectively. In my view this practice
should be strongly discouraged and an alternative philosophy adopted. I agree
that the 95% limits are too high and often give a false impression of
imprecision. The presentation of 50% likely limits, or "possible"
limits for the true effect represents a radical, yet welcome, departure from
conventional practice. A confidence interval is defined by the probability
that it contains the true or population value. Hence, a 50% CI is the
interval that you are 50% certain contains the value that would be estimated
from a much larger study. Confidence of only 50% may be an anathema to those
locked into testing null hypotheses, as, in their eyes, it is equivalent to
testing against the null hypothesis at a P value of 0.5. However, as stated,
this alternative philosophy should not be viewed as an analogue of significance
testing. In his
concluding sentence, the author doubts whether 50% likely limits will come
into widespread or, indeed, any use during his lifetime. As with the adoption
of analysis based on clinical significance, how fast and how far we progress
is in our own hands. Active engagement with the gatekeepers of knowledge–the
reviewers and journal editors–together with continuing education of
ourselves, our faculty peers, and our students, may help shift the paradigm.
