Thanks for pointing me in the right direction with these! My degree is really frequentist and slow paced, excited to get to work on this analysis.
Clarification on null hypothesis:
The null hypothesis is that there is no difference in effect on the dependent variable from the treatment and control variables. I am not assessing the truth of the null hypothesis because if it is true, then I can pick whichever one I want. If control is better, then picking treatment is negative utility. If treatment is better then picking control is negative utility. If the null is true, then I am free to do treatment or control without suffering in either case. Therefore I gain no utility from a test to see if the null is true or not.
Consider shifting your tone when people know less stats than you. Saying ” I suspect you don’t know what a null hypothesis is” makes people feel defensive and not willing to take your useful advice. Try saying “can you clarify what you mean by ____” or “here’s a common definition of a null hypothesis”.
Clarification on dependent variables:
I was going to code the outcome as 1 if sex|second date. The thinking is that only women who are attracted to me have sex with me (but may not want to date, for lots of good reasons). Meanwhile many women who are attracted to me do go on a second date. But few women are attracted to me but do neither sex nor a second date. Since attraction is the concept I want to explain, this should have the best specificity and sensitivity of available measures.
I am considering a second DV using eye contact during date (qualitative) as a robustness check. I think some people do lots of eye contact on all dates as a subconscious influence strategy, so it has more false positives than the other two.
Alright, it seems you do know what a null hypothesis is. Glad I could be of help.