Like they are on the same axis of some specific effect or result so they are somewhat statistically meaningful in determining the range of outputs or responses
Is orthogonality usually of a gradient or range like nature, is that essentially what is implied by orthogonality and the orthogonal items being on the same axis?
Edit- i think i might have misunderstood orthogonal
Wouldnt it be better to have all three? I feel like if you only do null and hypothesis you miss out on something seemingly opposite while still orthogonal or maybe thats nonsensical and I’m just too tired right now haha 🤪
Hypothesis testing is not of the form “what causes this?” What you’re suggesting seems to be along those lines.
Instead it’s more “can we say with high confidence that this specific factor causes this?”
That doesn’t mean you can’t test other factors! You can test them all with enough time and resources.
There are multi-factor statistical tools like ANOVA. But they still depend on you identifying what the factors might be.
But if you have factors A, B, C, and D in your analysis, and it’s actually the totally unknown factor E… you might find a lot of unexplained variance in your statistics, or you might mislead yourself into thinking it’s ABCD and never discover what E actually was.
But at the end of the day that’s just a fancier form of “does this specific thing cause this effect.”
And the essence of science is discovering Factor E and testing it, with new hypotheses.