Confidence intervals : better answers to better questions.

By Geoff Cumming, Fiona Fidler. 2009

96 citations recorded by the source corpus.

Subfields: Experimental and Cognitive Psychology, General Decision Sciences, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/c181e3c15fe400bc839d7bbb924705b52f1bafad

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In Experimental and Cognitive Psychology

John K. Kruschke

In General Decision Sciences

In Statistics and Probability

Citations in the corpus

Equivalence Tests (2017, unknown): “…due to the wide-spread use of identical measurements (e.g., the number of deaths, the amount of money spent), but in some subfields in psychology the variability in the measures that are collected require standardized effect sizes to make comparisons across studies (Cumming & Fidler, 2009).”
Bayesian estimation supersedes the t test. (2013, background): “…take the sampling distribution of the difference of sample means from the null hypothesis, denoted p(ȳ1−ȳ2|µ1−µ2 = 0), and re-center it on the observed difference of sample means, and then superimpose that distribution on the parameter axis, µ1 − µ2 (Cumming, 2007; Cumming & Fidler, 2009).”
Final Collapse of the Neyman-Pearson Decision Theoretic Framework and Rise of the neoFisherian (2009, background): “…1999, Anderson et al. 2000, Nicholls 2001, Kline 2004, Fidler et al. 2006, Wagenmakers 2007, Lukacs et al. 2007, McCarthy 2007, Ziliak & McCloskey 2008, Cumming & Fidler 2009) have recommended doing away with significance testing and reporting of P values altogether or at least in most situations .”
The Bayesian New Statistics: Hypothesis testing, estimation, meta-analysis, and power analysis from a Bayesian perspective (2017, unknown): “Cumming (e.g., Cumming, 2007, 2014; Cumming & Fidler, 2009) has discussed superimposing sampling distributions or relative likelihood curves on the CI, but neither of these approaches provides the probability of the parameter value, given the data.”
Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan (2014, methodology): “For example, in the context of estimating the mean of normally distributed data, Cumming and Fidler (2009, p. 18) stated, “In fact, the [sampling distribution of Mdiff from the null hypothesis], if centered on our [actual]Mdiff, rather than on μ as in [the sampling distribution], gives the relative ”