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Recognition memory experiments are an important source of empirical constraints for theories and models of memory. Unfortunately, standard methods for analyzing recognition memory data have problems that are often severe enough to prevent clear answers being obtained. A key example is whether longer lists lead to poorer recognition performance. The presence or absence of such a list length effect is critical test of competing itemand context-based theories of interference, but remains an unresolved empirical issue, largely because of the weaknesses of the standard analysis. In this paper, we develop a new Bayesian method of analysis that overcomes the problems. We report data from a new recognition memory experiment that manipulates list length, as well as the better understood manipulation of word frequency, and present both standard and Bayesian analyses of the data. The comparison of the two methods allows us to highlight the advantages of the Bayesian approach in inferring the values of psychologically meaningful variables, and in choosing between different models representing different theoretical assumptions about memory. In a typical yes/no recognition memory task, participants are asked to study a list of items and then decide whether or not each of a set of test items appeared on the study list. This task has been a touchstone for understanding episodic memory (Glanzer & Adams, 1985; Ratcliff, Clark, & Shiffrin, 1990), and has provided important constraint for a series of memory models (Gillund & Shiffrin, 1984; Murdock, 1982; Eich, 1982; Hintzman, 1986; Humphreys, Bain, & Pike, 1989; Shiffrin & Steyvers, 1997; McClelland & Chappell, 1998; Clark & Gronlund, 1996; Dennis & Humphreys, 2001). Recently, however, there has been debate concerning the primary source of interference in recognition memory paradigms. Logically, interference can arise either from the other items that appear in the study list, or from the other contexts in which a test item has appeared, or from both (Humphreys, Wiles, & Dennis, 1994). A critical empirical test of these competing theoretical positions involves the presence or absence of list length effects. If item noise is the primary source of interference, recognition should be poorer for longer study lists than for shorter ones. If context is the primary source of interference, changes in the length of the study list should not change recognition performance. There is no consensus on whether or not a list length effect is observed empirically. Dennis and Humphreys (2001) argued that, for verbal stimuli, context is the primary source of interference, and presented empirical evidence consistent with the absence of a list length effect. Cary and Reder (2003) contested this conclusion, and presented empirical evidence consistent with a list length effect using a remember-know paradigm. The source of interference is a fundamental aspect of understanding memory phenomena, and so this debate is crucial to the development of models of recognition memory. Unfortunately, the appropriate way to analyze recognition data has been a controversial topic (Banks, 1970; Lockhart & Murdock, 1970; Snodgrass & Corwin, 1988), because the methodology that is used standardly has a number of undesirable properties. These fall into two main classes: Those related to the application of Signal Detection Theory (SDT), and those related to the application of standard methods for statistical inference. In this paper, we accept the standard SDT assumptions, but develop a Bayesian framework for understanding recognition memory performance that improves how the model can be related to experimental data. In particular, we tackle both issues of parameter estimation caused by the standard use of frequentist methods, and issues of model selection and evaluation caused by the standard use of Null Hypothesis Significance Testing (NHST). We start by describing a new recognition memory experiment. We outline the standard method of analysis and, by applying it to the new data, describe its deficiencies. We then introduce and apply the Bayesian approach to the same data, and contrast its findings to the standard results. Finally, we relate these findings to our theoretical understanding of how memory works.