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      Alternatives to P value: confidence interval and effect size

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      Publication date (Electronic):
      Journal: Korean Journal of Anesthesiology
      Publisher: The Korean Society of Anesthesiologists
      Keywords: Confidence intervals, Effect sizes, P value

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          Abstract

          The previous articles of the Statistical Round in the Korean Journal of Anesthesiology posed a strong enquiry on the issue of null hypothesis significance testing (NHST). P values lie at the core of NHST and are used to classify all treatments into two groups: "has a significant effect" or "does not have a significant effect." NHST is frequently criticized for its misinterpretation of relationships and limitations in assessing practical importance. It has now provoked criticism for its limited use in merely separating treatments that "have a significant effect" from others that do not. Effect sizes and CIs expand the approach to statistical thinking. These attractive estimates facilitate authors and readers to discriminate between a multitude of treatment effects. Through this article, I have illustrated the concept and estimating principles of effect sizes and CIs.

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          Effect size, confidence interval and statistical significance: a practical guide for biologists.

          Shinichi Nakagawa, Innes Cuthill (2007)
          Null hypothesis significance testing (NHST) is the dominant statistical approach in biology, although it has many, frequently unappreciated, problems. Most importantly, NHST does not provide us with two crucial pieces of information: (1) the magnitude of an effect of interest, and (2) the precision of the estimate of the magnitude of that effect. All biologists should be ultimately interested in biological importance, which may be assessed using the magnitude of an effect, but not its statistical significance. Therefore, we advocate presentation of measures of the magnitude of effects (i.e. effect size statistics) and their confidence intervals (CIs) in all biological journals. Combined use of an effect size and its CIs enables one to assess the relationships within data more effectively than the use of p values, regardless of statistical significance. In addition, routine presentation of effect sizes will encourage researchers to view their results in the context of previous research and facilitate the incorporation of results into future meta-analysis, which has been increasingly used as the standard method of quantitative review in biology. In this article, we extensively discuss two dimensionless (and thus standardised) classes of effect size statistics: d statistics (standardised mean difference) and r statistics (correlation coefficient), because these can be calculated from almost all study designs and also because their calculations are essential for meta-analysis. However, our focus on these standardised effect size statistics does not mean unstandardised effect size statistics (e.g. mean difference and regression coefficient) are less important. We provide potential solutions for four main technical problems researchers may encounter when calculating effect size and CIs: (1) when covariates exist, (2) when bias in estimating effect size is possible, (3) when data have non-normal error structure and/or variances, and (4) when data are non-independent. Although interpretations of effect sizes are often difficult, we provide some pointers to help researchers. This paper serves both as a beginner's instruction manual and a stimulus for changing statistical practice for the better in the biological sciences.
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            Generalized eta and omega squared statistics: measures of effect size for some common research designs.

            Stephen Olejnik, James Algina (2003)
            The editorial policies of several prominent educational and psychological journals require that researchers report some measure of effect size along with tests for statistical significance. In analysis of variance contexts, this requirement might be met by using eta squared or omega squared statistics. Current procedures for computing these measures of effect often do not consider the effect that design features of the study have on the size of these statistics. Because research-design features can have a large effect on the estimated proportion of explained variance, the use of partial eta or omega squared can be misleading. The present article provides formulas for computing generalized eta and omega squared statistics, which provide estimates of effect size that are comparable across a variety of research designs.
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              T test as a parametric statistic

              Tae Kim (2015)
              In statistic tests, the probability distribution of the statistics is important. When samples are drawn from population N (µ, σ2) with a sample size of n, the distribution of the sample mean X̄ should be a normal distribution N (µ, σ2/n). Under the null hypothesis µ = µ0, the distribution of statistics z = X ¯ - µ 0 σ / n should be standardized as a normal distribution. When the variance of the population is not known, replacement with the sample variance s 2 is possible. In this case, the statistics X ¯ - µ 0 s / n follows a t distribution (n-1 degrees of freedom). An independent-group t test can be carried out for a comparison of means between two independent groups, with a paired t test for paired data. As the t test is a parametric test, samples should meet certain preconditions, such as normality, equal variances and independence.
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                Author and article information

                Journal
                Journal ID (nlm-ta): Korean J Anesthesiol
                Journal ID (iso-abbrev): Korean J Anesthesiol
                Journal ID (publisher-id): KJAE
                Title: Korean Journal of Anesthesiology
                Publisher: The Korean Society of Anesthesiologists
                ISSN (Print): 2005-6419
                ISSN (Electronic): 2005-7563
                Publication date (Print): December 2016
                Publication date (Electronic): 25 October 2016
                Volume: 69
                Issue: 6
                Pages: 555-562
                Affiliations
                Department of Anesthesiology and Pain Medicine, Guro Hospital, Korea University School of Medicine, Seoul, Korea.
                Author notes
                Corresponding author: Dong Kyu Lee, M.D., Ph.D. Department of Anesthesiology and Pain Medicine, Guro Hospital, Korea University School of Medicine, 148, Gurodong-ro, Guro-gu, Seoul 08308, Korea. Tel: 82-2-2626-3237, Fax: 82-2-2626-1438, entopic@ 123456naver.com
                Author information
                http://orcid.org/0000-0002-4068-2363
                Article
                DOI: 10.4097/kjae.2016.69.6.555
                PMC ID: 5133225
                PubMed ID: 27924194
                SO-VID: 7aa72ecf-8a01-4303-9bfe-c34a1295b4f2
                Copyright © Copyright © the Korean Society of Anesthesiologists, 2016
                License:

                This is an open-access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                Date received : 11 August 2016
                Date revision received : 13 September 2016
                Date accepted : 15 September 2016
                Categories
                Subject: Statistical Round

                ScienceOpen disciplines: Anesthesiology & Pain management
                Keywords: confidence intervals,effect sizes,p value
                Data availability:
                ScienceOpen disciplines: Anesthesiology & Pain management
                Keywords: confidence intervals, effect sizes, p value

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