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Open AccessJournal ArticleDOI

Confidence Intervals from Normalized Data: A correction to Cousineau (2005)

Richard D. Morey
- Vol. 4, Iss: 2, pp 61-64
TLDR
It is shown why this is the case and offered a simple correction that makes the expected size of Cousineau confidence intervals the same as that of Loftus and Masson confidence intervals.
Abstract
Presenting confidence intervals around means is a common method of expressing uncertainty in data. Loftus and Masson (1994) describe confidence intervals for means in within‐subjects designs. These confidence intervals are based on the ANOVA mean squared error. Cousineau (2005) presents an alternative to the Loftus and Masson method, but his method produces confidence intervals that are smaller than those of Loftus and Masson. I show why this is the case and offer a simple correction that makes the expected size of Cousineau confidence intervals the same as that of Loftus and Masson confidence intervals.

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References
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Journal ArticleDOI

Using confidence intervals in within-subject designs

TL;DR: It is argued that to best comprehend many data sets, plotting judiciously selected sample statistics with associated confidence intervals can usefully supplement, or even replace, standard hypothesis-testing procedures.
Journal ArticleDOI

Confidence intervals in within-subject designs: A simpler solution to Loftus and Masson's method

TL;DR: A simple alternative method is proposed that provides a single error bar for all the conditions, masking information such as the heterogeneity of variances across conditions and how it can be implemented in SPSS.
Journal ArticleDOI

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TL;DR: As a potential alternative to standard null hypothesis significance testing, 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 are described.
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Journal ArticleDOI

Relational and arelational confidence intervals: a comment on Fidler, Thomason, Cumming, Finch, and Leeman (2004).

TL;DR: Both arelational and relational CIs are presented, which provide a rough guide to variability in data, a coarse view of the replicability of patterns, and a quick check of the heterogeneity of variance.
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