Evidence-Based Statistics. Peter M. B. Cahusac

Pawitan Y. In All Likelihood: Statistical Modelling and Inference Using Likelihood. Oxford: Oxford University Press; 2001.

      10 10 Clayton D, Hills M. Statistical Models in Epidemiology. Oxford: Oxford University Press; 2013.

      11 11 Lindsey JK. Introductory Statistics: A Modelling Approach. Oxford: Clarendon Press; 1995.

      12 12 Kirkwood BR, Sterne JAC. Essential Medical Statistics. 2nd ed. Oxford: Blackwell; 2003.

      13 13 Armitage P, Berry G, Matthews JNS. Statistical Methods in Medical Research. Oxford: Wiley-Blackwell; 2002.

      14 14 Maxwell SE, Delaney HD. Designing Experiments and Analyzing Data: A Model Comparison Perspective. Belmont: Wadsworth Publishing Company; 1990.

      15 15 Judd CM, McClelland GH, Ryan CS. Data Analysis: A Model Comparison Approach to Regression, ANOVA, and Beyond (3rd ed): Routledge; 2017.

      16 16 Edwards AWF. Likelihood in statistics. In: Wright JD, editor. International Encyclopedia of the Social and Behavioral Sciences (2nd ed). Oxford: Elsevier; 2015. p. 116–9.

      17 17 Goodman SN. Toward evidence-based medical statistics. 1: The p value fallacy. Annals of Internal Medicine. 1999; 130 (12):995–1004.

      18 18 Goodman SN. Toward evidence-based medical statistics. 2: The Bayes factor. Annals of Internal Medicine. 1999; 130 (12):1005–13.

      19 19 Goodman SN, Royall R. Evidence and scientific research. American Journal of Public Health. 1988; 78 (12):1568–74.

      20 20 Dixon P. The effective number of parameters in post hoc models. Behavior Research Methods. 2013; 45(3):604–12.

      21 21 Goodman SN. Meta-analysis and evidence. Controlled Clinical Trials. 1989; 10(2):188–204.

      22 22 Dixon P. The p-value fallacy and how to avoid it. Canadian Journal of Experimental Psychology/Revue canadienne de psychologie expérimentale. 2003; 57(3):189–202.

      23 23 Glover S, Dixon P. Likelihood ratios: a simple and flexible statistic for empirical psychologists. Psychonomic Bulletin and Review. 2004; 11(5):791–806.

      24 24 Royall R. The likelihood paradigm for statistical evidence. In: Taper ML, Lele SR, editors. The Nature of Scientific Evidence. Chicago: University of Chicago; 2004. p. 119–52.

      25 25 de Winter P, Cahusac PMB. Starting Out in Statistics: An Introduction for Students of Human Health, Disease, and Psychology. Chichester: John Wiley & Sons; 2014.

      26 26 Cumming G, Calin-Jageman R. Introduction to the New Statistics. New York: Routledge; 2017.

      27 27 Fisher RA. Statistical Methods and Scientific Inference. Edinburgh: Oliver & Boyd; 1956.

      28 28 Edwards AWF. Statistical methods in scientific inference. Nature. 1969; 222 (5200):1233–7.

      29 29 Tsou T-S, Royall RM. Robust likelihoods. Journal of the American Statistical Association. 1995; 90 (429):316–20.

      30 30 Salsburg DS. The religion of statistics as practiced in medical journals. American Statistician. 1985; 39(3):220–3.

      31 31 Cohen J. Things I have learned (so far). American Psychologist. 1990; 45 (12):1304-12.

      32 32 Tukey JW. The philosophy of multiple comparisons. Statistical Science. 1991; 6(1):100–16.

      33 33 Baguley T. Standardized or simple effect size: what should be reported? British Journal of Psychology. 2009; 100(3):603–17.

Notes

      1 1 Taper and Lele (p. 545) emphasis added 'The evidential approach is alone … in having its measure of evidence invariant to intent, belief, and time of hypothesis formulation. The evidence is the evidence. Both belief and error probabilities have been separated from evidence. This is not to say that belief and error probabilities are unimportant in making inferences, but only that belief, error probabilities, and evidence can be most effectively used for inference if they are not conflated' [1].

      2 2 The counternull is the value on the other side of the sample mean that is equidistant from the sample mean as the null is from the sample mean. See Section 8.7.

      3 3 Often the secondary hypothesis will be the null hypothesis.

      4 4 There is an adjustment to Hedges' statistic for small samples, that means multiplying the value by (N − 3)/(N − 2.25), see p. 244 in [7].