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Inference by eye: Confidence intervals and how to read pictures of data
 American Psychologist
, 2005
"... Wider use in psychology of confidence intervals (CIs), especially as error bars in figures, is a desirable development. However, psychologists seldom use CIs and may not understand them well. The authors discuss the interpretation of figures with error bars and analyze the relationship between CIs a ..."
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Cited by 107 (14 self)
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Wider use in psychology of confidence intervals (CIs), especially as error bars in figures, is a desirable development. However, psychologists seldom use CIs and may not understand them well. The authors discuss the interpretation of figures with error bars and analyze the relationship between CIs and statistical significance testing. They propose 7 rules of eye to guide the inferential use of figures with error bars. These include general principles: Seek bars that relate directly to effects of interest, be sensitive to experimental design, and interpret the intervals. They also include guidelines for inferential interpretation of the overlap of CIs on independent group means. Wider use of interval estimation in psychology has the potential to improve research communication substantially. Inference by eye is the interpretation of graphically presented data. On first seeing Figure 1, what questions should spring to mind and what inferences are justified? We discuss figures with means and confidence intervals (CIs), and propose rules of eye to guide the interpretation of such figures. We believe it is timely to consider inference by eye because psychologists are now being encouraged to make greater use of CIs. Many who seek reform of psychologists ’ statistical practices advocate a change in emphasis from null hypothesis significance testing (NHST) to CIs, among other techniques
Comparing 2d vector field visualization methods: A user study. Visualization and Computer Graphics
 IEEE Transactions on
, 2005
"... Comparing 2D vector field visualization methods: A user study ..."
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Cited by 53 (7 self)
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Comparing 2D vector field visualization methods: A user study
Social Projection to Ingroups and Outgroups: A Review and MetaAnalysis
 Personality and Social Psychology Review 9
, 2005
"... Social projection is the tendency to expect similarities between oneself and others. A review of the literature and a metaanalysis reveal that projection is stronger when people make judgments about ingroups than when they make judgments about outgroups. Analysis of moderator variables further reve ..."
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Cited by 42 (1 self)
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Social projection is the tendency to expect similarities between oneself and others. A review of the literature and a metaanalysis reveal that projection is stronger when people make judgments about ingroups than when they make judgments about outgroups. Analysis of moderator variables further reveals that ingroup projection is stronger for laboratory groups than for real social categories. The mode of analysis (i.e., nomothetic vs. idiographic) and the order of judgments (i.e., self or group judged first) have no discernable effects. Outgroup projection is positive, but small in size. Together, these findings support the view that projection can serve as an egocentric heuristic for inductive reasoning. The greater strength of ingroup projection can contribute to ingroupfavoritism, perceptions of ingroup homogeneity, and cooperation with ingroup members. Social projection can be defined as a process, or a set of processes, by which people come to expect others to be similar to themselves. Associations between judgments about the self and judgments about the
HA: The incorporation of effect size in information technology, learning, and performance research
 Information Technology, Learning, and Performance Journal
"... Journal are expected to adhere to the publication guidelines of the American Psychological Association (2001) and generally accepted research and statistical methodology. This manuscript describes the rationale supporting the reporting of effect size in quantitative research and also provides exampl ..."
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Cited by 40 (0 self)
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Journal are expected to adhere to the publication guidelines of the American Psychological Association (2001) and generally accepted research and statistical methodology. This manuscript describes the rationale supporting the reporting of effect size in quantitative research and also provides examples of how to calculate effect size for some of the most common statistical analyses. We include a table of recommendations for effect size interpretation. We also address basic assumptions and cautions on the reporting of effect size.
Cautionary note on reporting etasquared values from multifactor ANOVA designs
 Educational and Psychological Measurement
, 2004
"... can be found at:Educational and Psychological MeasurementAdditional services and information for ..."
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Cited by 38 (4 self)
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can be found at:Educational and Psychological MeasurementAdditional services and information for
Evaluating statistical difference, equivalence, and indeterminacy using inferential confidence intervals: An integrated alternative method of conducting null hypothesis statistical tests
 Psychological Methods
, 2001
"... Null hypothesis statistical testing (NHST) has been debated extensively but always successfully defended. The technical merits of NHST are not disputed in this article. The widespread misuse of NHST has created a human factors problem that this article intends to ameliorate. This article describes a ..."
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Cited by 37 (0 self)
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Null hypothesis statistical testing (NHST) has been debated extensively but always successfully defended. The technical merits of NHST are not disputed in this article. The widespread misuse of NHST has created a human factors problem that this article intends to ameliorate. This article describes an integrated, alternative inferential confidence interval approach to testing for statistical difference, equivalence, and indeterminacy that is algebraically equivalent to standard NHST procedures and therefore exacts the same evidential standard. The combined numeric and graphic tests of statistical difference, equivalence, and indeterminacy are designed to avoid common interpretive problems associated with NHST procedures. Multiple comparisons, power, sample size, test reliability, effect size, and causeeffect ratio are discussed. A section on the proper interpretation of confidence intervals is followed by a decision rule summary and caveats. The longstanding controversy surrounding null hypothesis statistical testing (NHST) has typically been argued on its technical merits, and they are not dis
Misinterpretations of significance. A problem students share with their teachers
 Methods of Psychological Research Online
, 2000
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Recommendations for increasing replicability in psychology
"... Replicability of findings is at the heart of any empirical science. The aim of this article is to move the current replicability debate in psychology toward concrete recommendations for improvement. We focus on research practices, but also offer guidelines for reviewers, editors, journal management, ..."
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Cited by 22 (0 self)
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Replicability of findings is at the heart of any empirical science. The aim of this article is to move the current replicability debate in psychology toward concrete recommendations for improvement. We focus on research practices, but also offer guidelines for reviewers, editors, journal management, teachers, granting institutions, and university promotion committees, highlighting some of the emerging and existing practical solutions that can facilitate implementation of these recommendations. The challenges for improving replicability in psychological science are systemic. Improvement can occur only if changes are made at many levels of practice, evaluation, and reward. Replicability 2 Preamble The purpose of this article is to recommend sensible improvements that can be implemented in
The null ritual: What you always wanted to know about null hypothesis testing but were afraid to ask
 Handbook on Quantitative Methods in the Social Sciences. Sage, Thousand Oaks, CA
, 2004
"... No scientific worker has a fixed level of significance at which from year to year, and in all circumstances, he rejects hypotheses; he rather gives his mind to each particular case in the light of his evidence and his ideas. (Ronald A. Fisher, 1956, p. 42) It is tempting, if the only tool you have i ..."
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Cited by 21 (1 self)
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No scientific worker has a fixed level of significance at which from year to year, and in all circumstances, he rejects hypotheses; he rather gives his mind to each particular case in the light of his evidence and his ideas. (Ronald A. Fisher, 1956, p. 42) It is tempting, if the only tool you have is a hammer, to treat everything as if it were a nail. (A. H. Maslow, 1966, pp. 15–16) One of us once had a student who ran an experiment for his thesis. Let us call him Pogo. Pogo had an experimental group and a control group and found that the means of both groups were exactly the same. He believed it would be unscientific to simply state this result; he was anxious to do a significance test. The result of the test was that the two means did not differ significantly, which Pogo reported in his thesis. In 1962, Jacob Cohen reported that the experiments published in a major psychology journal had, on average, only a 50: 50 chance of detecting a mediumsized effect if there was one. That is, the statistical power was as low as 50%. This result was widely cited, but did it change researchers’ practice? Sedlmeier and Gigerenzer (1989) checked the studies in the same journal, 24 years later, a time period that should allow for change. Yet only 2 out of 64 researchers mentioned power,
The epistemology of mathematical and statistical modeling: A quiet methodological revolution
 American Psychologist
, 2010
"... A quiet methodological revolution, a modeling revolution, has occurred over the past several decades, almost without discussion. In contrast, the 20th century ended with contentious argument over the utility of null hypothesis significance testing (NHST). The NHST controversy may have been at leas ..."
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Cited by 19 (0 self)
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A quiet methodological revolution, a modeling revolution, has occurred over the past several decades, almost without discussion. In contrast, the 20th century ended with contentious argument over the utility of null hypothesis significance testing (NHST). The NHST controversy may have been at least partially irrelevant, because in certain ways the modeling revolution obviated the NHST argument. I begin with a history of NHST and modeling and their relation to one another. Next, I define and illustrate principles involved in developing and evaluating mathematical models. Following, I discuss the difference between using statistical procedures within a rulebased framework and building mathematical models from a scientific epistemology. Only the former is treated carefully in most psychology graduate training. The pedagogical implications of this imbalance and the revised pedagogy required to account for the modeling revolution are described. To conclude, I discuss how attention to modeling implies shifting statistical practice in certain progressive ways. The epistemological basis of statistics has moved away from being a set of procedures, applied mechanistically, and moved toward building and evaluating statistical and scientific models.