1985
DOI: 10.2307/2530899
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Theory of Probability.

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Cited by 255 publications
(256 citation statements)
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“…This is due to the fact that null hypothesis significance testing only allows us to reject the null hypothesis, but not to state positive evidence in favor of it. In order to ensure that our data genuinely represent evidence that orthographic information from one word does not influence the processing of another word, we conducted a Bayesian analysis on the data for selected measures from both Experiment 1 and Experiment 2, by calculating Bayes factors (Jeffreys, 1961;Kass & Raftery, 1995;Rouder, Morey, Speckman, & Province, 2012). Bayes factors allow us to assess the extent to which a data set supports one of two competing hypotheses (e.g.…”
Section: Resultsmentioning
confidence: 99%
“…This is due to the fact that null hypothesis significance testing only allows us to reject the null hypothesis, but not to state positive evidence in favor of it. In order to ensure that our data genuinely represent evidence that orthographic information from one word does not influence the processing of another word, we conducted a Bayesian analysis on the data for selected measures from both Experiment 1 and Experiment 2, by calculating Bayes factors (Jeffreys, 1961;Kass & Raftery, 1995;Rouder, Morey, Speckman, & Province, 2012). Bayes factors allow us to assess the extent to which a data set supports one of two competing hypotheses (e.g.…”
Section: Resultsmentioning
confidence: 99%
“…As shown in the first and second rows of Table 1, the answer is yes: The overall effect size (Hedges’ g ) is 0.09, combined z = 6.33, p = 1.2 × 10 -10 . The Bayesian BF value is 5.1 × 10 9 , greatly exceeding the criterion value of 100 that is considered to constitute “decisive evidence” for the experimental hypothesis ( Jeffreys, 1998). Moreover, the BF value is robust across a wide range of the scaling factor r , ranging from a high value of 5.1 × 10 9 when we set r = 0.1 to a low value of 2.0 × 10 9 when r = 1.0.…”
Section: Resultsmentioning
confidence: 87%
“…Jeffreys (1998) has suggested the following verbal labels for interpreting BF levels of p (H 1 )/ p (H 0 ): BF = 1 – 3:        Worth no more than a bare mentionBF = 3 – 10:      Substantial evidence for H 1 BF = 10 – 30:    Strong evidence for H 1 BF = 30 – 100:  Very Strong evidence for H 1 BF > 100:          Decisive evidence for H 1 …”
Section: Methodsmentioning
confidence: 99%
“…That is, when using a Frequentist approach, failures to reject the null-hypothesis are inconclusive; they indicate only lack of evidence for the alternative hypothesis but do not 21 CAN TRAINING CHANGE ATTENTIONAL BREADTH allow estimation of evidence for the null-hypothesis (Kruschke, 2011;Mulder & Wagenmakers, in press). In contrast, a Bayesian approach allows comparison of evidence between different models as it relies on likelihood ratios, allowing us to accumulate evidence in favor of the null-hypothesis (Jeffreys, 1961). 4 To determine how confident we can be that attentional breadth was not successfully broadened using the one-or multisession attentional breadth training, we re-analyzed our attentional breadth assessment data using two-sided Bayesian t-tests.…”
Section: Bayesian Comparison Of the Null And Experimental Hypothesesmentioning
confidence: 99%
“…4 As such, the Bayes Factor (BF) -i.e., the ratio of the probability of the data given one hypothesis compared to the probability of the data given another hypothesis -allows to draw conclusions concerning the strength of evidence for the alternative hypothesis [BF (10) (Jeffreys, 1961;Wagenmakers, Wetzels, Borsboom, & van der Maas, 2011). Beck et al, 1996); PA and NA state, positive and negative affect (PANAS; Watson et al, 1988).…”
Section: Footnotesmentioning
confidence: 99%