alexa Statistical and clinical significance, and how to use confidence intervals to help interpret both.


Evidence based Medicine and Practice

Author(s): Fethney J

Abstract Share this page

Abstract Statistical significance is a statement about the likelihood of findings being due to chance. Classical significance testing, with its reliance on p values, can only provide a dichotomous result - statistically significant, or not. Limiting interpretation of research results to p values means that researchers may either overestimate or underestimate the meaning of their results. Very often the aim of clinical research is to trial an intervention with the intention that results based on a sample will generalise to the wider population. The p value on its own provides no information about the overall importance or meaning of the results to clinical practice, nor do they provide information as to what might happen in the future, or in the general population. Clinical significance is a decision based on the practical value or relevance of a particular treatment, and this may or may not involve statistical significance as an initial criterion. Confidence intervals are one way for researchers to help decide if a particular statistical result (whether significant or not) may be of relevance in practice. Copyright 2010 Australian College of Critical Care Nurses Ltd. Published by Elsevier Ltd. All rights reserved. This article was published in Aust Crit Care and referenced in Evidence based Medicine and Practice

Relevant Expert PPTs

Relevant Speaker PPTs

Recommended Conferences

Relevant Topics

Peer Reviewed Journals
Make the best use of Scientific Research and information from our 700 + peer reviewed, Open Access Journals
International Conferences 2017-18
Meet Inspiring Speakers and Experts at our 3000+ Global Annual Meetings

Contact Us

© 2008-2017 OMICS International - Open Access Publisher. Best viewed in Mozilla Firefox | Google Chrome | Above IE 7.0 version