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

伸展

Confidence Intervals,en applies it to the (common) confidence interval for the sample mean. It then contrasts the concepts of null hypothesis significance testing and confidence intervals. Finally it considers confidence intervals for dependent (paired) samples.

愛(ài)國(guó)者

MELON

Factorial Analysis of Variance (ANOVA),olved factors. The chapter concludes with a brief outlook on the case of three (or more) factors. The logic of factorial ANOVA is also required for understanding ANOVA with repeated measures (Chap. .).

chance

Repeated-Measures Analysis of Variance (ANOVA),ethod of calculation using so-called “ipsative” scores. Subsequently, we introduce the general procedure for calculating a repeated-measures ANOVA with one single factor, which is strikingly similar to a two-way ANOVA as discussed in Chap. ..

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Bayesian Alternatives,re give an introduction to the basic concept and logic of Bayesian hypothesis testing, and we discuss key advantages and disadvantages of these procedures. In addition to references to the Bayesian literature, the chapter also contains a compact presentation of common software solutions for implementing Bayesian procedures.

忙碌

Concluding Remarks,ion should be known when assuming the null hypothesis to be true. This distribution allows for determining whether the empirical observations are sufficiently unlikely to reject the null hypothesis and decide in favor of the alternative hypothesis instead.

Cacophonous

esearch.Formal writing styles and aspects for advanced stude.What actually is a p-value for? And what is a significant result? This book answers such questions by providing a compact introduction to inferential statistics. Our major focus is on the logic of inferential statistics and hypothesis test

PLUMP

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Hypothesis Testing and Significance,eses. Deciding between these two hypotheses is possible based on the result of a significance test. In this context, we introduce the logic underlying such decisions using a simulation, including concepts such as significance level and .. The logic described here will also be the cornerstone for almost all statistical tests discussed in this book.

Capitulate

Difference Hypotheses for Up to Two Means: ,Tests, for (two) dependent (i.e., paired) samples, and the one-sample .-test. The first two tests examine whether an empirical difference in means is also indicative of differences in population means. The third test compares a sample mean with an assumed population mean.