Table 1.

Bayes Factors for Logistic Models.

	Memory Set Size	Response Set Size List	Response Set Size New
Large-Pool Experiment	1.07 × 1010 [1.00–1.77 × 1010]	1.85 × 1011 [1.16–3.36 × 1011]	0.37 [0.048–0.49]
Small-Pool Experiment	4.14 × 1010 [3.29–7.21 × 1010]	2.65 × 108 [0.78–2.69 × 108]	14.2 [1.8–16.4]

Note: The Bayes factor reflects the strength of evidence for keeping the effect in question in the model over excluding it. It expresses the factor by which we should multiply the ratio of our prior probabilities assigned to the competing models to obtain our ratio of posterior probabilities. Bayes Factors are based on Cauchy priors on standardized effect sizes with a scale of .353; the range of Bayes Factors for scales between 0.25 and 3.0 obtained from the sensitivity analysis is given in brackets.