I've been looking for an introductory book in statistics and experiment design for a methods course for psychology students. I think I've been looking through at least 30 books and all go through the same old motions. Fist hypothesis testing, then t-test, Anova, two-Way Anova and ordinary least square regression. And all of them insist on explaining these models by tedious calculations. A t-test requires you to calculate a t-statistic, an Anova requires you to sum squares of this and that, regression instead is done my minimizing the squared error.

From my perspective I find it much easier and intuitive to understand these types of models from a likelihood perspective. For example, a t-test is the model $y \sim \text{Normal}(\beta_0 + \beta_1x,\sigma^2)$ and an Anova is $y \sim \text{Normal}(\beta_0 + \beta_1x_1 + \beta_2x_2 + \ldots,\sigma^2)$ where $x, x_1, x_2$ are dummy coded variables. To fit such a model one would then use, for example, maximum likelihood which I find intuitive and easy to explain. This kind formulation also makes the connection between a t-test, an ANOVA and regression trivial to understand.

So what I've been looking for an introductory book in statistics and experiment design that teaches statistics from this likelihood perspective. I would be very grateful for any suggestion!


2 Answers 2


James K. Lindsey has several books in similar vein. Perhaps the one nearest to your goal is



It's probably not quite what you have in mind, but Dan Navarro has written a textbook for his own undergraduate statistics subject in psychology. I have read it all, but it seems to use the approach you mention on occasion. It uses a fair bit of R and generally seems to be more sophisticated than your average intro psych stats textbook. The PDF of the chapters are available online for free.

Daniel Navarro: Learning Statistics with R


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