I’m not aware of a single course/book that meets all your needs, but let me try to tackle them point by point.
For 1, 3, 5, and 7, I would strongly recommend “Improving your Statistical Inferences” by Daniel Lakens. It’s extremely accessible, present by video on Coursera, totally free, and covers lots of other really useful material such as likelihood-based inference, Bayes, effect sizes, and the importance of replication to testing a hypothesis. Very little math (some R), more conceptual but presented in an accessible way.
For 2, 3, and 6 - these seem to be more analysis-specific questions for which you might need a general textbook explaining different types of tests. Any intro stats textbook (the non-math heavy ones used in my field are typically “Discovering Statistics with SPSS/R” by Andy Field) will generally start to answer 2 and 6 in particular. Typically these types of books will give you some discrete type of decision-making chart (e.g. if this type of data, this type of experimental manipulation, then this test)
Let me give a little hint here though - I think it might be good to start learning generalised linear modelling strategies early, as these give you a lot of flexibility analysing almost anything. This is what Statistical Rethinking is really good for (among may other things), but there’s plenty of other resources available.