Results 291 to 300 of about 607,887 (313)

Statistical Methods for Replicability Assessment

2022
The reproducibility of scientific discoveries is a hallmark of scientific research. Although its centrality is widely appreciated in the scientific community, precise definitions for reproducibility and quantitative approaches for replicability assessment are still lacking.
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Science with or without statistics: Discover-generalize-replicate? Discover-replicate-generalize?

Behavioral and Brain Sciences, 2022
AbstractOverstated generalizability (external validity) is common in research. It may coexist with inflation of the magnitude and statistical support for effects and dismissal of internal validity problems. Generalizability may be secured before attempting replication of proposed discoveries or replication may precede efforts to generalize.
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Replicated measurements and algebraic statistics

2009
A basic application of algebraic statistics to design and analysis of experiments considers a design as a zero-dimensional variety and identifies it with the ideal of the variety. Then, a subset of a standard basis of the design ideal is used as support for identifiable regression models. Estimation of the model parameter is performed by standard least
NOTARI, ROBERTO, E. Riccomagno
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Reassessing Replication: Addressing the Replication Crisis from a Statistical Perspective

2023
VCU Theses and ...
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Mutation–Replication Statistics of Polymerase Chain Reactions

Journal of Computational Biology, 2002
The variability of the products of polymerase chain reactions, due to mutations and to incomplete replications, can have important clinical consequences. Sun (1995) and Weiss and von Haeseler (1995) modeled these errors by a branching process and introduced estimators of the mutation rate and of the efficiency of the reaction based, for example, on the
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A Useful Statistical Technique for Replication Studies

Journal for Research in Mathematics Education, 1977
Eastman's (1975) recent expression in the JRME of concern over so few replication studies is a concern that should be shared by all who have an interest in research. Replication is critical in assessing the "significance" of research results. A correlation coefficient of .20, which is statistically significant at the .01 level, will be much more ...
Randy Ellsworth, Richard L. Isakson
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Statistics and replication experiments: Comparing interaction effects

Cognitive Therapy and Research, 1989
A method is presented that allows the comparison and combination of interaction effects obtained in two or more experiments. Using an analysis of variance model, we first evaluated the interaction effects found in experiments conducted by Alloy and Abramson (1979, Experiment 2) and Bryson, Doan, and Pasquali (1984).
Victor A. Benassi, Robert F. Belli
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Replication, statistical consistency, and publication bias

Journal of Mathematical Psychology, 2013
The paper starts with exposing recurrent flaws in psychological science that lead some critics to announce a scientific crisis. Some researchers, either fraudulently or by ignorance, behave in such a way as to invalidate their conclusions. The most common practices include withholding null results and only reporting conclusive experiments, selecting ...
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Statistical Methods for Analyzing Microarray Feature Data with Replications

Journal of Computational Biology, 2003
Expression levels in oligonucleotide microarray experiments depend on a potentially large number of factors, for example, treatment conditions, different probes, different arrays, and so on. To dissect the effects of these factors on expression levels, fixed-effects ANOVA methods have previously been proposed.
Yaning, Yang, Josephine, Hoh, Clemens, Broger, Martin, Neeb, Joanne, Edington, Klaus, Lindpaintner, Jurg, Ott   +6 more
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Experimental toxicology: Issues of statistics, experimental design, and replication

NeuroToxicology, 2017
The difficulty of replicating experiments has drawn considerable attention. Issues with replication occur for a variety of reasons ranging from experimental design to laboratory errors to inappropriate statistical analysis. Here we review a variety of guidelines for statistical analysis, design, and execution of experiments in toxicology.
Wayne, Briner, Jeral, Kirwan
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