Inferential Statistics
University of Amsterdam via Coursera
Overview
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Inferential statistics are concerned with making inferences based on relations found in the sample, to relations in the population. Inferential statistics help us decide, for example, whether the differences between groups that we see in our data are strong enough to provide support for our hypothesis that group differences exist in general, in the entire population.
We will start by considering the basic principles of significance testing: the sampling and test statistic distribution, p-value, significance level, power and type I and type II errors. Then we will consider a large number of statistical tests and techniques that help us make inferences for different types of data and different types of research designs. For each individual statistical test we will consider how it works, for what data and design it is appropriate and how results should be interpreted. You will also learn how to perform these tests using freely available software.
For those who are already familiar with statistical testing: We will look at z-tests for 1 and 2 proportions, McNemar's test for dependent proportions, t-tests for 1 mean (paired differences) and 2 means, the Chi-square test for independence, Fisher’s exact test, simple regression (linear and exponential) and multiple regression (linear and logistic), one way and factorial analysis of variance, and non-parametric tests (Wilcoxon, Kruskal-Wallis, sign test, signed-rank test, runs test).
We will start by considering the basic principles of significance testing: the sampling and test statistic distribution, p-value, significance level, power and type I and type II errors. Then we will consider a large number of statistical tests and techniques that help us make inferences for different types of data and different types of research designs. For each individual statistical test we will consider how it works, for what data and design it is appropriate and how results should be interpreted. You will also learn how to perform these tests using freely available software.
For those who are already familiar with statistical testing: We will look at z-tests for 1 and 2 proportions, McNemar's test for dependent proportions, t-tests for 1 mean (paired differences) and 2 means, the Chi-square test for independence, Fisher’s exact test, simple regression (linear and exponential) and multiple regression (linear and logistic), one way and factorial analysis of variance, and non-parametric tests (Wilcoxon, Kruskal-Wallis, sign test, signed-rank test, runs test).
Taught by
Emiel van Loon and Annemarie Zand Scholten
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Reviews
3.2 rating, based on 4 reviews
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Ericdo1810 completed this course, spending 4 hours a week on it and found the course difficulty to be hard.
This course is awesome on so many levels. This is the best inferential statistics course I've come across. Here's why: - The slides are beautiful and visually appealing, making following the rigorous content easier to digest. - Instructors are captivating... -
Brandt Pence is taking this course right now, spending 3 hours a week on it and found the course difficulty to be very easy.
dropped dropped dropped dropped dropped dropped dropped dropped dropped dropped dropped dropped dropped dropped dropped dropped dropped dropped dropped dropped -
Rachael Walker30
Rachael Walker completed this course.
You must pay to access certain content of the course. This trend in Coursera is really a pity, and I am actively looking for other platforms for high quality MOOCs -
Beniamino Di Maro4
Beniamino Di Maro completed this course.
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