Titlebook: Understanding Inferential Statistics; From A for Significa Markus Janczyk,Roland Pfister Textbook 20231st edition Springer-Verlag GmbH Germ

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Confidence Intervals,g estimators are usually called point estimators, but we can go one step further and specify a range for plausible population parameters. This procedure is called interval estimation, and the calculated intervals are labeled .. This chapter introduces the general form of a confidence interval and th

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Repeated-Measures Analysis of Variance (ANOVA),seen as generalizing the .-test for two dependent samples to more than two samples (or conditions). Accordingly, the present focus is on the performance changes . each participant again. In order to understand the principle of repeated-measures ANOVA, the chapter begins by considering a simplified m

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Correlation and Regression,ype of correlation hypotheses. We begin with clarifying what a statistical relationship (or statistical dependence) actually is. Next, we derive the correlation coefficient as a measure to capture linear relationships. This is followed by a take on simple linear regression, which is another way of a

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Textbook 20231st edition statistics. Our major focus is on the logic of inferential statistics and hypothesis testing: We cover the logic behind statistical tests, we then walk through the most common procedures (t-test, analysis of variance with and without repeated measures, correlation/regression) and discuss pitfalls o

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Introduction to Inferential Statistics 1: Random Variables,ariables, which is most relevant for the methods described in later chapters. For continuous random variables, we specifically focus on the normal distribution, which plays a central role in inferential statistics.