2. The nature of the variables/scores involved; and
3. How the data were collected.
That's pretty terse, but this seemingly simple list covers a lot of different judgments as to how best to analyze the data.
There are various decision trees available that can help you walk through a lot of the usual (e.g., basic, intermediate) statistical test options, but none will be a complete compendium of all tests available (here's an example: https://www.microsiris.com/Statistical%20Decision%20Tree/). As well, talking through your data aims and research project with a local expert may help you to identify an alternative approach that you might not have considered, but might perform better to achieve your research goal/s.
2. The nature of the variables/scores involved; and
3. How the data were collected.
That's pretty terse, but this seemingly simple list covers a lot of different judgments as to how best to analyze the data.
There are various decision trees available that can help you walk through a lot of the usual (e.g., basic, intermediate) statistical test options, but none will be a complete compendium of all tests available (here's an example: https://www.microsiris.com/Statistical%20Decision%20Tree/). As well, talking through your data aims and research project with a local expert may help you to identify an alternative approach that you might not have considered, but might perform better to achieve your research goal/s.
Where it suggests that the dependent variable (DV) is assumed to be normal, bear in mind that it is typically the model errors that are assumed to be normal, not the DV. (I've left a comment for the UCLA folks suggesting that they clarify this.) And bear in mind it just a guideline. HTH.
Related to your query and for gaining concepts for statistical sampling test, I would like to suggest you the links (for hypothesis, p-value and test respectively): https://www.khanacademy.org/math/statistics-probability/significance-tests-one-sample/more-significance-testing-videos/v/small-sample-hypothesis-test