No abstract

As a potential alternative to standard null hypothesis significance testing, we describe methods for graphical presentation of data -particularly condition means and their corresponding confidence intervals -for a wide range of factorial designs used in experimental psychology. We describe and illustrate confidence intervals specifically appropriate for between-subject versus within-subject factors. For designs involving more than two levels of a factor, we describe the use of contrasts for graphical illustration of theoretically meaningful components of main effects and interactions. These graphical techniques lend themselves to a natural and straightforward assessment of statistical power.Null hypothesis significance testing (NHST), although hotly debated in the psychological literature on statistical analysis (e.g., Chow, 1998;Cohen, 1990Cohen, , 1994Hagen, 1997;Hunter, 1997;Lewandowsky & Maybery, 1998;Loftus, 1991Loftus, , 1993Loftus, , 1996Loftus, , 2002Schmidt, 1996), is not likely to go away any time soon (Krueger, 2001). Generations of students from multiple disciplines continue to be schooled in the NHST approach to interpreting empirical data, and practicing scientists rely almost reflexively on the logic and methods associated with it. Our goal here is not to extend this debate, but rather to enhance understanding of a particular alternative to NHST for interpreting data. In our view, to the extent that a variety of informative means of constructing inferences from data are made available and clearly understood, researchers will increase their likelihood of forming appropriate conclusions and communicating effectively with their audiences.A number of years ago, we advocated and described computational approaches to the use of confidence intervals as part of a graphical approach to data interpretation (Loftus & Masson, 1994; see also, Loftus, 2002). The power and effectiveness of graphical data presentation is undeniable (Tufte, 1983) and is common in all forms of scientific communication in experimental psychology and in other fields. In many instances, however, plots of descriptive statistics (typically means) are not accompanied by any indication of variability or stability associated with those descriptive statistics. The diligent reader, then, is forced to refer to a dreary accompanying recital of significance tests to determine how the pattern of means should be interpreted.It has become clear through interactions with colleagues and from queries we have received about the use of confidence intervals in conjunction with graphical presentation of data, that more information is needed about practical, computational steps involved in generating confidence intervals, particularly with respect to designs involving interactions among variables. In this article, we briefly explain the logic behind confidence intervals for both between-subject and within-subject designs, then move to a consideration of a range of multifactor designs wherein interaction effects are of interest. Methods for comp...

Weapon focus" refers to the concentration of a crime witness's attention on a weapon, and the resultant reduction in ability to remember other details of the crime. We examined this phenomenon by presenting subject-witnesses with a series of slides depicting an event in a fast-food restaurant. Haif of the subjects saw a customer point a gun at the cashier; the other half saw him hand the cashier a check. In Experiment 1, eye movements were recorded while subjects viewed the slides. Results showed that subjects made more eye fixations on the weapon than on the check, and fixations on the weapon were of a longer duration than fixations on the check. In Experiment 2, the memory of subjects in the weapon condition was poorer than the memory of subjects in the check condition: In Experiment I similar, though only marginally significant, performance effects were obtained. These results provide the first direct empirical support for weapon focus.

The principle focus of this paper is on interpretation of interactions that are obtained when response probability is used as a dependent variable. It is argued that results obtained with probability (or any dependent variable) are only interesting insofar~s they reflect something about a corresponding theoretical component. It follows that the functional mapping of response probability (which is measured) onto the state of a theoretical component (which is inferred) must be somehow specified if conclusions are to be meaningful. Depending on the nature of such a mapping, various types of results, particularly results involving interactions, mayor may not be interpretable.

Many people believe that information that is stored in long-term memory is permanent, citing examples of "retrieval techniques" that are alleged to uncover previously forgotten information. Such techniques include hypnosis, psychoanalytic procedures, methods for eliciting spontaneous and other conscious recoveries, and-perhaps most important-the electrical stimulation of the brain reported by Wilder Penfield and his associates. In this article we first evaluate • the evidence and conclude that, contrary to apparent popular belief, the evidence in no way confirms the view that all memories are permanent and thus potentially recoverable. We then describe some failures that resulted from attempts to elicit retrieval of previously stored information and conjecture what circumstances might cause information stored in memory to be irrevocably destroyed. This article was written while E. Loftus was a fellow at the Center for Advanced Study in the Behavioral Sciences, Stanford, California, and G. Loftus was a visiting scholar in the Department of Psychology at Stanford University. James Fries generously picked apart an earlier version of this article. Paul Baltes translated the writings of Johann Nicolas Tetens (177?).