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An Introduction to Clinical Research$
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Piers Page, James Carr, William Eardley, David Chadwick, and Keith Porter

Print publication date: 2011

Print ISBN-13: 9780199570072

Published to Oxford Scholarship Online: November 2020

DOI: 10.1093/oso/9780199570072.001.0001

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(p.139) Chapter 8 Statistics
An Introduction to Clinical Research

James Carr

Oxford University Press

Here, we’ll attempt to provide an introduction to what statistics are, some key concepts, and some of the more common tests used in clinical research. It is not a definitive chapter—whole books exist detailing just one of the tests we’ll talk about here and it’s not likely that you have the time or inclination for that! Rather, it is an attempt to help you think about the uses and limitations of statistics and how they might fit into the overall process of your research design. In clinical research much of the data you will collect will be numerical. Collecting the data is, however, just the first stage—then, you have to make sense of it. This is where statistics come into play. The science of collecting and interpreting numerical data, statistics can be used to describe data, such as by calculating averages and distributions ( descriptive statistics) or to draw inferences by analysing patterns and relationships within the data ( inferential statistics). Inferential statistics will usually form the main part of any analysis. Statistical analysis hinges on the use of sampling. In clinical research it is rarely possible to examine whole populations and as a result a sample is drawn from the relevant population. The difficulty with this is that one can never be certain that the sample is representative of the population as a whole and so that some form of bias is not operating. Good experimental design can minimize but not eliminate this possibility; consequently there will always be an element of doubt as to whether a genuine effect is being observed or if we are simply witnessing random variations in a data set. Statistics allow us to analyse patterns within the sample data and to draw inferences about the wider population. One must always bear in mind that statistical tests are used to determine if a prediction we make can actually be supported. They do not provide actual proof that we are correct; if our theory holds up statistically, we are less likely to be incorrect. Equally, if our theory were incorrect the statistics would be unlikely to support it.

Keywords:   bar chart, central tendency, distribution, effect size, histograms, multiple regression, null hypothesis, outliers, parametric tests, range

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