Effect size, confidence interval and statistical significance: a practical guide for biologists
Tóm tắt
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
Từ khóa
Tài liệu tham khảo
American Psychological Association, 2001, Publication Manual of the American Psychological Association
Burnham K. P., 2002, Model Selection and Multimodel Inference: a Practical Information‐Theoretic Approach
Clark J. S., 2001, Design and Analysis of Ecological Experiments, 327, 10.1093/oso/9780195131871.003.0017
Cohen J, 1988, Statistical Power Analysis for the Behavioral Sciences
Crawley M. J, 2002, Statistical Computing: an Introduction to Data Analysis Using S‐Plus
Dixon P. M, 2001, Design and Analysis of Ecological Experiments, 267, 10.1093/oso/9780195131871.003.0014
Dobson A. J, 2002, An Introduction to Generalized Linear Models
Faraway J. J, 2005, Linear Models with R
Faraway J. J, 2006, Extending the Linear Model with R
Fisher R. A, 1935, The Design of Experiments
Fletcher T. D, 2007, The psychometric Package: Appliced Psychometric Theory
Fleiss J. L, 1994, The Handbook of Research Synthesis, 245
Fox J, 2002, An R and S‐Plus Companion to Applied Regression
Gabbay D. M., 2002, Handbook of the logic of argument and inference
Gage N. L, 1978, The Scientific Basis of the Art of Teaching
Gelman A., 2007, Data Analysis Using Regression and Multilevel/Hierarchical Models
Glass G. V, 1976, Review of Research in Education Vol. 5, 351
Grafen A., 2002, Modern Statistics for the Life Sciences
Grissom R. J., 2005, Effect Sizes for Research: a Broad Practical Approach
Gurevitch J., 1993, Design and Analysis of Ecological Experiments, 347
Harlow L. L., 1997, What If There Were No Significance Tests?
Hedges L., 1985, Statistical Methods for Meta‐Analysis
Hopkins W. G. (2004).New View of Statistics.http://www.sportsci.org/resource/stats/.
Hunt M, 1997, How Science Takes Stock: the Story of Meta‐Analysis
Hunter J. E., 2004, Methods of Meta-Analysis: Correcting Error and Bias in Research Finding, 10.4135/9781412985031
Lipsey M. W., 1993, The efficacy of psychological educational, and behavioral treatment: conformation from meta‐analysis, American Psychologist, 48, 1181, 10.1037/0003-066X.48.12.1181
Lipsey M. W., 2001, Practical Meta‐Analysis
Maindonald J., 2003, Data Analysis and Graphics Using R: an Example‐Based Approach
Manly B. R. J, 2007, Randomization, Bootstrap and Monte Carlo Methods in Biology
Rice J. A, 1995, Mathematical Statistics and Data Analysis
Rosenberg M. S., 2000, MetaWin: Statistical Software for Meta‐Analysis
Rosenthal R, 1994, The Handbook of Research Synthesis, 231
Rosenthal R., 2000, Contrasts and Effect Sizes in Behavioral Research: A Correlational Approach
Shadish W. R., 1994, Handbook of Research Synthesis, 261
Shadish W. R., 1999, ES
Snijders T., 1999, Multilevel Analysis: an Introduction to Basic and Advanced Multilevel Modeling
Woodworth G. G, 2004, Biostatistics: a Bayesian Introduction
Zar J, 1999, Biostatistical Analysis