What is Reddit's opinion of Discovering Statistics Using IBM SPSS Statistics: North American Edition?

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When it comes to stats, and most technical fields, learning resources often suffer from a tradeoff between being technically accurate, being accessible, and skills related to practical implementation. I'm going to lean a bit towards accessible and practical here because, for technical accuracy, then the best bet is honestly to do a major or minor in your university's stats department (or at least to read some of the textbooks they assign).

Books. There's a number of good books on introductory statistics. If you can deal with his slightly rude sense of humor, Andy Field has a few good books on stats that focus on either SPSS or R. When it comes to choosing the right statistical program, this will depend a bit on your goals. SPSS is accessible, but expensive and limited (i.e. the learning curve is very gentle, but it has an expensive annual license and it can be difficult to implement something that IBM didn't make a GUI for). R, on the other hand, is less accessible, but free and versatile (i.e. it takes time to learn how to work with R, but you can accomplish quite a bit with it).

Also, since you're premed, by the time you're a physician machine learning may have become a much more dominant paradigm in data analysis (or perhaps we'll have decided it's a fad, I have no idea). But, if you want to get a jump start on that, this book provides a good practical introduction, and this book provides the technical detail.

YouTube. There's a couple of YouTube channels that make stats videos of general interest. A few that come to mind are Khan Academy and Statquest. There are a ton of other YouTube channels which focus on topic and methods of interest to the channel owner.

There are certainly some statistical methods that you'll see in almost any medical specialty (e.g. chi-squared, logistic regression). However, some areas of research tend to use other specific methodologies simply because of the types of questions they tend to ask. For example, cancer research will often use survival analyses (e.g. Cox regression), health administration/policy research may use various econometric methods (e.g. regression discontinuity, instrumental variables), epidemiology will use advanced survey methods (e.g. multistage, stratified, weighted surveys), etc. So a good starting point will be to read papers in the fields that interest you, and see which statistical methods keep popping up.

Blogs/Forums. There are a number of good blogs and forums which focus on some aspect of data analysis:

Cross-Validated. This tends to be a bit heavy on the technical detail - and every meme you've seen related to programmer humor on Reddit applies here - but you still may find some gold nuggets.

R-Bloggers. This focuses on implementing projects in R.

TowardsDataScience. This provides simple overviews (admittedly too simple, sometimes) of data science concepts.

Data Colada. This focuses more on the social sciences, but the principles of good data analysis don't really change.

Andrew Gelman's blog. This is a bit more technical and focuses more on political science and economics, but again, the good data analysis is good data analysis.

Statistical Tools for High-Throughput Data Analysis. This focuses on tutorials in R.