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Furthermore, the nature in which Dienes examines the logic, assumptions and inferences of the most frequently used statistical tests allows us as researchers to ensure we are employing the most rigorous of approaches within our research. This is an exceptional overview of the scientific principles that underpin the discipline, and should be welcomed by teacher, researcher and undergraduate psychology student alike.
Everyone who wishes to be clear about how well any scientific position is supported by data will want to be sure they understand the ideas presented in this book. Highly recommended for students and professionals alike.
McClelland, Stanford University, USA 'Students should have - and perhaps need to have - a deeper understanding of how theories are tested and evolve. Likewise, most researchers would be well served by a deeper understanding of the logic, assumptions, and implications of our commonly used statistical procedures. Dienes' book speaks well to needs, providing a sophisticated and clear tour of the conceptual and philosophical foundations of psychological research.
I enjoyed the book and will surely be influenced by it in my teaching. Dienes relates statistical controversies to general issues in the philosophy of science, and in so doing puts common misconceptions right. The book is full of advice that makes the difference between a mediocre and expert researcher. Despite some difficult passages I was drawn into the story; imagine that when reading about statistics - remarkable!
In sum: A very useful correction to our typical methods courses for advanced undergraduates, graduates, and even many established researchers. Dienes makes topics that are often dull interesting, covers positions he does not favour fairly and comprehensively, and describes all the important issues succinctly. In short, a really nice, brief, but comprehensive account of the important issues underlying psychology understanding. A textbook on issues in psychological research methods that actually explains how science works, why it has the exciting texture it does and what philosophical principles underlie it.
It will change the way research methods are taught.
It provides an authoritative and lucid treatment of the scientific nature of psychology that will appeal to undergraduates and anyone else interested in the tussle between science and irrationality. The desire to do good science, and to avoid poor inferences, is infectious. Found in Data Science. Never miss a course! Add as "Interested" to get notified of this course's next session. Go to class. Start now for free! Sign up.
Statistical inference is the process of drawing conclusions about populations or scientific truths from data. There are many modes of performing inference including statistical modeling, data oriented strategies and explicit use of designs and randomization in analyses.
Furthermore, there are broad theories frequentists, Bayesian, likelihood, design based, … and numerous complexities missing data, observed and unobserved confounding, biases for performing inference. A practitioner can often be left in a debilitating maze of techniques, philosophies and nuance. This course presents the fundamentals of inference in a practical approach for getting things done. After taking this course, students will understand the broad directions of statistical inference and use this information for making informed choices in analyzing data.
Taught by Brian Caffo. Tags usa. Browse More Coursera Articles. Browse More Data Science courses.
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This course is basically an introduction to statistics in R. The course covers many different topics in the span of 4 weeks from basic probability and distributions to T tests, p values and statistical power. The lectures take the form of slideshows with a lot of dense mathematical notation, small text and mediocre voiceovers.
The course tries to cover too much ground too fast and the material isn't presented in a way that is easy to understand or engaging. Full Review. Was this review helpful to you? Statistical Inference is the sixth course in the Data Science specialization, and the first course in the analytical portion of the course followed by Regression Models and Practical Machine Learning. The course covers probability, variance, distributions normal, binomial, poisson , hypothesis testing and p-values, power, multiple comparisons, and finally resampling.
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Overall this is a rather poor introduction to statistical methods, and the only really relevant hypothesis test covered is the simple t-test. This is the first course taught by Brian Caffo, who is more mathematicall…. This is the first course taught by Brian Caffo, who is more mathematically-inclined, and he doesn't do a particularly good job of explaining the material in an intuitive way.
There are a few good portions of the course, though, and I though the explanation of statistical power using the manipulate package in R was particularly good, and quite a bit better than the coverage I've received in face-to-face university courses I've taken. Otherwise, though, this course in no way will prepare students to actually conduct most common statistical tests, and it doesn't cover non-parametric statistics in any depth whatsoever.
Understanding Psychology as a Science: an introduction to scientific and statistical inference
I have heard good things about the former Duke statistics course on Coursera, and that course has just at the time of this writing been released as a new specialization Statistics with R , so that might be a better choice for learners looking for a better coverage of statistics using R packages. Overall, three stars. There are a few gems hidden among the rest of the course content, but overall the course is not particularly good for learning statistical techniques, and it is unlikely that you'll come away from this with any real understanding of how to apply statistical hypothesis testing unless you have pre-existing experience in this area.
[NEWS] Understanding Psychology as a Science: An Introduction to Sc…
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