In medical research, it is important to be able to examine whether there is a significant difference between two samples. With this, establishing an appropriate hypothesis is a critical, basic step for correct interpretation of results in inferential statistical data analysis. It is important to note that the aim of hypothesis testing is not to “accept” or “reject” the null hypothesis but to gauge the likelihood that the observed difference is genuine if the null hypothesis is true.
Traditionally, the null hypothesis assumes that there is no statistically significant difference between the two groups. It has become more difficult to develop new treatments that are better than the standard of care. This review article summarizes and explains the methodology of the different types of clinical trials regarding the relevant basic statistical concepts and hypothesis testing.
From the Department of Medicine, Division of Physical Medicine and Rehabilitation, University of Toronto, Toronto, Ontario, Canada (DK); and Toronto Rehabilitation Institute, University Health Network, Toronto, Ontario, Canada (MA, JF).
All correspondence should be addressed to: Dinesh Kumbhare, MD, PhD, FRCPC, FAAPMR, Department of Medicine, Division of Physical Medicine and Rehabilitation, University of Toronto, Toronto, Ontario, Canada.
Financial disclosure statements have been obtained, and no conflicts of interest have been reported by the authors or by any individuals in control of the content of this article.