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Summary: The end-in-view is doing good science and avoiding junk science, which is proliferating. Adjusting standards, etc. are only means to an end. There are no silver bullets or magic wands. Doing good science depends on good design, accurate measurement, and replication. Statistical Modeling, Causal Inference, and Social ScienceWhen considering proposals for redefining or abandoning statistical significance, remember that their effects on science will only be indirect! Andrew Gelman | Professor of Statistics and Political Science and Director of the Applied Statistics Center, Columbia University
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Mike Norman considers the following as important: junk science, scientific method, statistical significance, statistics
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Summary: The end-in-view is doing good science and avoiding junk science, which is proliferating. Adjusting standards, etc. are only means to an end. There are no silver bullets or magic wands. Doing good science depends on good design, accurate measurement, and replication. Statistical Modeling, Causal Inference, and Social ScienceWhen considering proposals for redefining or abandoning statistical significance, remember that their effects on science will only be indirect! Andrew Gelman | Professor of Statistics and Political Science and Director of the Applied Statistics Center, Columbia University
Topics:
Mike Norman considers the following as important: junk science, scientific method, statistical significance, statistics
This could be interesting, too:
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Summary: The end-in-view is doing good science and avoiding junk science, which is proliferating. Adjusting standards, etc. are only means to an end. There are no silver bullets or magic wands. Doing good science depends on good design, accurate measurement, and replication.
Statistical Modeling, Causal Inference, and Social Science
When considering proposals for redefining or abandoning statistical significance, remember that their effects on science will only be indirect!
Andrew Gelman | Professor of Statistics and Political Science and Director of the Applied Statistics Center, Columbia University
When considering proposals for redefining or abandoning statistical significance, remember that their effects on science will only be indirect!
Andrew Gelman | Professor of Statistics and Political Science and Director of the Applied Statistics Center, Columbia University