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45
Using confidence intervals in withinsubject designs
 Psychonomic Bulletin & Review
, 1994
"... Wolford, and two anonymous reviewers for very useful comments on earlier drafts of the manuscript. Correspondence may be addressed to ..."
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Cited by 178 (21 self)
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Wolford, and two anonymous reviewers for very useful comments on earlier drafts of the manuscript. Correspondence may be addressed to
The earth is round (p < .05
 American Psychologist
, 1994
"... After 4 decades of severe criticism, the ritual of null hypothesis significance testing—mechanical dichotomous decisions around a sacred.05 criterion—still persists. This article reviews the problems with this practice, including its nearuniversal misinterpretation ofp as the probability that Ho is ..."
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Cited by 113 (0 self)
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After 4 decades of severe criticism, the ritual of null hypothesis significance testing—mechanical dichotomous decisions around a sacred.05 criterion—still persists. This article reviews the problems with this practice, including its nearuniversal misinterpretation ofp as the probability that Ho is false, the misinterpretation that its complement is the probability of successful replication, and the mistaken assumption that if one rejects Ho one thereby affirms the theory that led to the test. Exploratory data analysis and the use of graphic methods, a steady improvement in and a movement toward standardization in measurement, an emphasis on estimating effect sizes using confidence intervals, and the informed use of available statistical methods is suggested. For generalization, psychologists must finally rely, as has been done in all the older sciences,
Detecting group differences: Mining contrast sets
 Data Mining and Knowledge Discovery
, 2001
"... A fundamental task in data analysis is understanding the differences between several contrasting groups. These groups can represent different classes of objects, such as male or female students, or the same group over time, e.g. freshman students in 1993 through 1998. We present the problem of mini ..."
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Cited by 78 (3 self)
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A fundamental task in data analysis is understanding the differences between several contrasting groups. These groups can represent different classes of objects, such as male or female students, or the same group over time, e.g. freshman students in 1993 through 1998. We present the problem of mining contrast sets: conjunctions of attributes and values that differ meaningfully in their distribution across groups. We provide a search algorithm for mining contrast sets with pruning rules that drastically reduce the computational complexity. Once the contrast sets are found, we postprocess the results to present a subset that are surprising to the user given what we have already shown. We explicitly control the probability of Type I error (false positives) and guarantee a maximum error rate for the entire analysis by using Bonferroni corrections.
Using confidence intervals for graphically based data interpretation
 CANADIAN JOURNAL OF EXPERIMENTAL PSYCHOLOGY
, 2003
"... 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 il ..."
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Cited by 65 (15 self)
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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 betweensubject versus withinsubject 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.
Psychology will be a much better science when we change the way we analyze data
 Current Directions in Psychological Science
, 1996
"... because I believed that within it dwelt some of the most fundamental and challenging problems of the extant sciences. Who could not be intrigued, for example, by the relation between consciousness and behavior, or the rules guiding interactions in social situations, or the processes that underlie de ..."
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Cited by 22 (2 self)
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because I believed that within it dwelt some of the most fundamental and challenging problems of the extant sciences. Who could not be intrigued, for example, by the relation between consciousness and behavior, or the rules guiding interactions in social situations, or the processes that underlie development from infancy to maturity? Today, in 1996, my fascination with these problems is undiminished. But I've developed a certain angst over the intervening thirtysomething years—a constant, nagging feeling that our field spends a lot of time spinning its wheels without really making all that much progress. This problem shows up in obvious ways—for instance, in the regularity with which findings seem not to replicate. It also shows up in subtler ways—for instance, one doesn't often hear Psychologists saying, "Well this problem is solved now; let's move on to the next one " (as, for example, Johannes Kepler must have said over three centuries ago, after he had cracked the problem of describing planetary motion). I've come to believe that at least part of this problem revolves around our tools—particularly the tools that we use in the critical domains of data analysis and data interpretation. What we do, I sometimes feel, is akin to trying to build a violin using a stone mallet and a chainsaw. The tooltotask fit is not all that good, and as a result, we wind up building a lot of poorquality violins. My purpose here is to elaborate on these issues. In what follows, I will summarize our major dataanalysis and datainterpretation tools, and describe what I believe to be amiss with them. I will then offer some suggestions for change.
Sample Size for Multiple Regression: Obtaining Regression Coefficients That Are Accurate, Not Simply Significant
"... An approach to sample size planning for multiple regression is presented that emphasizes accuracy in parameter estimation (AIPE). The AIPE approach yields precise estimates of population parameters by providing necessary sample sizes in order for the likely widths of confidence intervals to be suffi ..."
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Cited by 12 (8 self)
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An approach to sample size planning for multiple regression is presented that emphasizes accuracy in parameter estimation (AIPE). The AIPE approach yields precise estimates of population parameters by providing necessary sample sizes in order for the likely widths of confidence intervals to be sufficiently narrow. One AIPE method yields a sample size such that the expected width of the confidence interval around the standardized population regression coefficient is equal to the width specified. An enhanced formulation ensures, with some stipulated probability, that the width of the confidence interval will be no larger than the width specified. Issues involving standardized regression coefficients and random predictors are discussed, as are the philosophical differences between AIPE and the power analytic approaches to sample size planning. Sample size estimation from a power analytic perspective is often performed by mindful researchers in order to have a reasonable probability of obtaining parameter estimates that are statistically significant. In general, the social sciences have slowly become more aware of the problems associated with underpowered studies and their corresponding Type II errors, which can yield misleading results in a given
Effect sizes and p values: What should be reported . . . ?
, 1996
"... Despite publication of many wellargued critiques of null hypothesis testing (NHT), behavioral science researchers continue to rely heavily on this set of practices. Although we agree with most critics' catalogs of NHT's flaws, this article also takes the unusual stance of identifying virtues that ..."
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Cited by 8 (0 self)
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Despite publication of many wellargued critiques of null hypothesis testing (NHT), behavioral science researchers continue to rely heavily on this set of practices. Although we agree with most critics' catalogs of NHT's flaws, this article also takes the unusual stance of identifying virtues that may explain why NHT continues to be so extensively used. These virtues include providing results in the form of a dichotomous (yes/no) hypothesis evaluation and providing an index (p value) that has a justifiable mapping onto confidence in repeatability of a null hypothesis rejection. The mostcriticized flaws of NHT can be avoided when the importance of a hypothesis, rather than the p value of its test, is used to determine that a finding is worthy of report, and when p = .05 is treated as insufficient basis for confidence in the replicability of an isolated nonnull finding. Together with many recent critics of NHT, we also urge reporting of important hypothesis tests in enough descriptive detail to permit secondary uses such as metaanalysis.
The Earth is spherical (p < 0.05): alternative methods of statistical inference
 Theoritical Issues in Ergonomics Science
, 2000
"... A literature review was conducted to understand the limitations of wellknown statistical analysis techniques, particularly analysis of variance. The review is structured around six major points: (1) averaging across participants can be misleading; (2) strong predictions are preferable to weak predi ..."
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Cited by 4 (0 self)
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A literature review was conducted to understand the limitations of wellknown statistical analysis techniques, particularly analysis of variance. The review is structured around six major points: (1) averaging across participants can be misleading; (2) strong predictions are preferable to weak predictions; (3) constructs and measures should be distinguished conceptually and empirically; (4) statistical signi ® cance and practical signi ® cance should be distinguished conceptually and empirically; (5) the null hypothesis is virtually never true; and (6) one experiment is always inconclusive. Based on these insights, a number of lesserknown and lessfrequently used statistical analysis techniques were identi ® ed to address the limitations of more traditional techniques. In addition, a number of methodological conclusions about the conduct of human factors research are presented. 1.
On Model Evaluation, Indices of Importance, and Interaction Values in Rough Set Analysis
, 2002
"... As most data models, "Computing with words" uses a mix of methods to achieve its aims, including several measurement indices. In this paper we discuss some proposals for such indices in the context of rough set analysis and present some new ones. In the first ..."
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Cited by 4 (2 self)
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As most data models, "Computing with words" uses a mix of methods to achieve its aims, including several measurement indices. In this paper we discuss some proposals for such indices in the context of rough set analysis and present some new ones. In the first