Statistical pearls: Importance of effect-size, blinding, randomization, publication bias, and the overestimated p-values

Statistical pearls: Importance of effect-size, blinding, randomization, publication bias, and the... In this issue of the Scandinavian Journal of Pain, our associate editor for medical statistics, Professor Eva Skovlund focuses on some of the important, and frequently misunderstood, basic issues of statistical analyses of medical research data [1].1The over-rated p-value is often misunderstoodp-values are always there in scientific publications from medical researchers. They impress reviewers, editors, and readers alike, more than they deserve. A low p-value is taken as a “proof” of the truth of the research-question, but the p-value only indicates the likelihood of finding the observed difference between treatments by random/by chance, even if there is no difference. Still, the observed difference can be of limited clinical relevance, in spite of a low p-value [1].2Effect-size and clinical significanceAs Eva Skovlund writes in her review [1]: “a p-value does neither assess the size of an effect as such nor whether a statistically significant result is of any clinical relevance”.For clinically meaningful information from the statistical analyses, the size of the effect and its 95% confidence interval are needed.The effect-size is the difference between the observed value(s) and the expected value(s). If the null-hypothesis is true, i.e. that there is no difference between the therapies, the effect-size will be zero. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Scandinavian Journal of Pain de Gruyter

Statistical pearls: Importance of effect-size, blinding, randomization, publication bias, and the overestimated p-values

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Publisher
de Gruyter
Copyright
© 2013 Scandinavian Association for the Study of Pain
ISSN
1877-8860
eISSN
1877-8879
D.O.I.
10.1016/j.sjpain.2013.08.001
Publisher site
See Article on Publisher Site

Abstract

In this issue of the Scandinavian Journal of Pain, our associate editor for medical statistics, Professor Eva Skovlund focuses on some of the important, and frequently misunderstood, basic issues of statistical analyses of medical research data [1].1The over-rated p-value is often misunderstoodp-values are always there in scientific publications from medical researchers. They impress reviewers, editors, and readers alike, more than they deserve. A low p-value is taken as a “proof” of the truth of the research-question, but the p-value only indicates the likelihood of finding the observed difference between treatments by random/by chance, even if there is no difference. Still, the observed difference can be of limited clinical relevance, in spite of a low p-value [1].2Effect-size and clinical significanceAs Eva Skovlund writes in her review [1]: “a p-value does neither assess the size of an effect as such nor whether a statistically significant result is of any clinical relevance”.For clinically meaningful information from the statistical analyses, the size of the effect and its 95% confidence interval are needed.The effect-size is the difference between the observed value(s) and the expected value(s). If the null-hypothesis is true, i.e. that there is no difference between the therapies, the effect-size will be zero.

Journal

Scandinavian Journal of Painde Gruyter

Published: Oct 1, 2013

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