I second the notion of getting an introductory statistics textbook (doesn't need to be in medicine or public health). I'll give two qualifiers, though. 1) Get an applied statistics book that has real data examples you can use, especially if you don't have the mathematics background. Do the problems in it with an analysis software and some by hand. 2) Make sure the author of the book has a Ph.D. in Statistics or Biostatistics (an MS would be okay too). Epidemiology/public health/psychology/nursing/medical degrees won't make the author an expert in statistics, despite that they may have experience with stats. I'm not trying to knock anyone without a statistics degree, but this is based on readily observable information. The literature is rife with examples of bad statistics from non-statisticians, including teaching things that are flat out incorrect.
If you have a book from a qualified author, you can be much more confident in what you're learning. If you have a truly good foundation, you can mobilize that knowledge from field to field (i.e. an applied stats book from business written by someone with a PhD in Stats can allow you to take those ideas and apply them to biomedical research). After you've nailed down some of the fundamentals, I would also recommend learning multiple linear regression (and simple, obviously). You can then move on to logistic regression or survival analysis. The latter two are very common in biomedical research also, but the former will make these easier to understand the others.