Power

Shravan Vasishth () and Michael Broe ()
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Shravan Vasishth: University of Potsdam, Department of Linguistics
Michael Broe: Ohio State University, 1304 Museum of Biological Diversity, Department of Evolution, Ecology & Organismal Biology

Chapter Chapter 4 in The Foundations of Statistics: A Simulation-based Approach, 2011, pp 81-96 from Springer

Abstract: Abstract Let’s assume we do an experiment, compute the t-value and p-value for our sample, and based on these values, reject the null hypothesis. As we mentioned in the previous chapter, and as you can prove to yourself through simulated replication of experiments, due to the very nature of random sampling it is always possible to stumble on a ‘rogue sample’, one whose statistic happens to be far from the population parameter. In this case it would, in fact, be an error to reject the hypothesis, though we wouldn’t know it. The technical name for this is a Type I error: the null hypothesis is true, but our sample leads us to reject it.

Keywords: Null Hypothesis; Error Probability; Error Type; Increase Sample Size; Null Result (search for similar items in EconPapers)
Date: 2011
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-642-16313-5_4

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DOI: 10.1007/978-3-642-16313-5_4

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