Yeah thats spot on; no need to state null hypothesis! More on reddit.com

The usage of "hypothesis" is definitely ambiguous, so it's not surprising that you're having trouble with it. There are people (or people in specific situations) who will use "hypothesis" and "prediction" interchangeably, but you'll also find other people (or even the same people just in other situations) who are more likely to use "hypothesis" interchangeably with "model" as a way to describe something at a higher level that's more generalizable and mechanistic than a "prediction." Some people will differentiate all three terms, which is maybe the best way to think about it. It's sort of a logical series. A "model" is something very broad and fundamentally mechanistic, but difficult to apply to the real world because it's so pared down. A "hypothesis" is a narrower form of the "model" that you arrive at by thinking about how the "model" should apply in a given system. And a "prediction" is the most detailed thing, anticipating the actual observation you will record if your "hypothesis" is a correct derivation of a correct "model." So, for example, from the "model" of Newtonian physics (which includes gravity, conservation of momentum, etc.) we could derive the "hypothesis" that a ball will fly further if you hit it harder. Then from that you can make the specific "prediction" that if you hit a ball with 50 units of force it will fly 125 units of distance. The "prediction" could be even more detailed, for that matter, including units of friction and angles of impact, for example. And there are actually many levels of "hypothesis," some more specific and some more general, that connect the "model" to the "prediction" The real key here is to figure out where along this continuous logical series the person you're talking to places these markers "model," "hypothesis," and "prediction." I'd say your advisor seems to want hear broader and more mechanistic ideas and thinks that what you're calling your hypotheses are too specific to your research or study system. More on reddit.com

A hypothesis is your first expectation for a specific research question before doing the research. For example, you do research on the relationship between hours worked and tiredness, your hypothesis could be: there is a positive relation between hours worked and tiredness. This expectation does however need to be informed. You read up on previous research to do so. A theory on the other hand is a framework to explain a phenomenon. Its the scientific consensus based upon a whole lot of research. It being a framework to explain makes it useful to look at a theory to formulate your hypothesis. If the theory states that people normally get tired after working long hours, you use that in yoir hypothesis. A theory is however not a law, and thus there always has to be a small chance of it being proven wrong. So science requires to have its current knowledge to be checked every now and then. Thats one of the reasons to reconduct research on already researched topics. Aside from that redoing research can also help develop the theory or general knowledge on a subject further. Perhaps people do indeed get tired after working long, but your new research shows that this is influenced by something else. In short: A hypothesis is used for your specific research and you use theory to form one. A theory is the accumulation of a lot of research and explains the bigger picture And because our knowledge has to be kept up to date and corrected if wrong, you reconduct research thats already been done More on reddit.com

This seems like the kind of thing there are lots of fairly interchangeable names for. I’d typically call it bootstrapping, and I’d reserve Monte Carlo HT for generating samples according to a parametric model. I’d also caution you to be careful about correlations between datapoints when using this kind of method (or any other p-test I suppose). While returns are a pretty reasonably place to apply these methods, he later suggests one could do the same with volatility. This would not be a good use imho. It’s well understood that volatility is serially correlated and just plucking random samples is not properly taking this into account. These kinds of issues come up regularly in finance and are one of the most difficult aspects of hypothesis testing in this domain. A particularly common issue is splitting up trading opportunities, if you have say a 100 trades in a day and you’re pnling them to the close splitting them into two groups according to some rule and then doing a standard hypothesis test on the mean is going to give you a completely useless result. More on reddit.com