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2016 ; 61
(2
): 137-45
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Biostatistics Series Module 2: Overview of Hypothesis Testing
#MMPMID27057011
Hazra A
; Gogtay N
Indian J Dermatol
2016[Mar]; 61
(2
): 137-45
PMID27057011
show ga
Hypothesis testing (or statistical inference) is one of the major applications of
biostatistics. Much of medical research begins with a research question that can
be framed as a hypothesis. Inferential statistics begins with a null hypothesis
that reflects the conservative position of no change or no difference in
comparison to baseline or between groups. Usually, the researcher has reason to
believe that there is some effect or some difference which is the alternative
hypothesis. The researcher therefore proceeds to study samples and measure
outcomes in the hope of generating evidence strong enough for the statistician to
be able to reject the null hypothesis. The concept of the P value is almost
universally used in hypothesis testing. It denotes the probability of obtaining
by chance a result at least as extreme as that observed, even when the null
hypothesis is true and no real difference exists. Usually, if P is < 0.05 the
null hypothesis is rejected and sample results are deemed statistically
significant. With the increasing availability of computers and access to
specialized statistical software, the drudgery involved in statistical
calculations is now a thing of the past, once the learning curve of the software
has been traversed. The life sciences researcher is therefore free to devote
oneself to optimally designing the study, carefully selecting the hypothesis
tests to be applied, and taking care in conducting the study well. Unfortunately,
selecting the right test seems difficult initially. Thinking of the research
hypothesis as addressing one of five generic research questions helps in
selection of the right hypothesis test. In addition, it is important to be clear
about the nature of the variables (e.g., numerical vs. categorical; parametric
vs. nonparametric) and the number of groups or data sets being compared (e.g.,
two or more than two) at a time. The same research question may be explored by
more than one type of hypothesis test. While this may be of utility in
highlighting different aspects of the problem, merely reapplying different tests
to the same issue in the hope of finding a P < 0.05 is a wrong use of statistics.
Finally, it is becoming the norm that an estimate of the size of any effect,
expressed with its 95% confidence interval, is required for meaningful
interpretation of results. A large study is likely to have a small (and therefore
"statistically significant") P value, but a "real" estimate of the effect would
be provided by the 95% confidence interval. If the intervals overlap between two
interventions, then the difference between them is not so clear-cut even if P <
0.05. The two approaches are now considered complementary to one another.
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