Textbook with list of hypothesis tests and practical guidance on use I found a list of statistical tests along with practical guidance on Wikipedia.
Can anyone point me to something similar, but in a textbook form? 
I'm interested in particular in practical guidance (along the lines of "should have n>30 for Z-test")
 A: Statistical Rules of Thumb (Wiley, 2002), by van Belle, has a lot of useful rules of thumb for applied statistics.
A: In general, I would have a look at statistics books in your domain of application (e.g., whether it is psychology, ecology, medical, sociology, etc.).
Such books tend to have less rigour.
Instead, such books often try to give useful decision rules to assist researchers where statistics is not the main interest of the researcher.
Here are a few suggestions coming from a behavioural and social sciences perspective.
Multivariate books
If you want practical tips on techniques like multiple regression, factor analysis, PCA, and so forth, these books are options:


*

*Hair et al Multivariate data analysis: This has very few formulas but lots of flow charts and simple decision rules designed to assist less-mathematically inclined social scientists implement multivariate stats.

*Tabachnick and Fidell Multivariate Statistics. This arguably has more rigour than Hair et al, but it does have sections devoted to giving practical advice.


SPSS Cookbook


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*I know a lot of psychology research students who are looking for a cookbook approach (perhaps just to get themselves started) to analysing their data turn to the SPSS Survival Manual. However, this is SPSS centric and more about tips on implementing analyses in SPSS.

A: For a thorough overview of tests, I can recommend the Handbook of Parametric and Nonparametric Statistics by David Sheskin.
A: Biometry,
by Sokal and Rohlf
has a fairly comprehensive table with such information on the inside of the front and back covers, but these tables apparently didn't make (perhaps due to placement) into Google's digitized version.
A: Could this help?

The following table shows general guidelines for choosing a statistical analysis. We emphasize that these are general guidelines and should not be construed as hard and fast rules.

From the UCLA Stata/SAS/R tutorial pages. I use a revised version in class.
