%A Hazra, Avijit
%A Gogtay, Nithya
%T Biostatistics series module 2: Overview of hypothesis testing
%9 IJDŽ Module on Biostatistics and Research Methodology for the Dermatologist - Module Editor: Saumya Panda
%D 2016
%J Indian Journal of Dermatology
%R 10.4103/0019-5154.177775
%P 137-145
%V 61
%N 2
%U http://www.e-ijd.org/article.asp?issn=0019-5154;year=2016;volume=61;issue=2;spage=137;epage=145;aulast=Hazra
%8 March 1, 2016
%X 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.
%0 Journal Article
%I Wolters Kluwer Medknow Publications
%@ 0019-5154