Titlebook: Understanding Statistics and Experimental Design; How to Not Lie with Michael H. Herzog,Gregory Francis,Aaron Clarke Textbook‘‘‘‘‘‘‘‘ 2019

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Understanding Replicationndings. The basic idea is simple. Unless the power is very high, we know that even real effects will not always produce significant outcomes simply due to random sampling. If power is only moderate but all studies are significant, the reported results seem too good to be true. Our considerations hav

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Suggested Improvements and Challengesstical reporting (e.g., the reported .-value does not match the reported .-value) are common, results presented at conferences are changed for journal publications, and researchers admit to publication bias and other poor practices. These problems have motivated many researchers to propose counterac

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Textbook‘‘‘‘‘‘‘‘ 2019s.  Part I makes key concepts in statistics readily clear. Parts I and II give an overview of the most common tests (t-test, ANOVA, correlations) and work out their statistical principles. Part III provides insight into meta-statistics (statistics of statistics) and demonstrates why experiments ofte

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Experimental Design: Model Fits, Power, and Complex Designsns are also easier to interpret, which can be a problem in many complex designs. In this chapter, we also show how to compute the power of an experiment, which is for example important to determine the sample size of your experiment.

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The Multiple Testing Problemhe population of trees in three regions of the world. In this case, a multiple testing problem arises that increases the risk of making a Type I error..In this chapter, we will introduce the multiple testing problem and present Bonferroni corrections as one (rather suboptimal) way to cope with it.

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