91–100 of 111 posts
Earlier quoted context omitted.
Publishing only significant results is a terrible idea in the first place. Publishing should be based on how interesting the design of the experiment was, not how interesting the result was.
P-value doesn't measure interestingness. If p>0.05 there was no result at all.
Wow so many comments that just don't get the criticism and want to continue with their magical beliefs about null hypothesis testing. This is a damning criticism of the most common techniques used by my scientists. The answer isn't to shrug and keep doing the same thing as before.
Questions like how many samples to collect, what methods to fit the uncertain data etc.
The core problem with this is: - DON'T is very clear and specific. Don't say "Stat-Sig", don't conclude causal effect, don't conclude anything based on p>0.05. - DO is very vague and unclear. Do be thoughtful, do accept uncertainty, do consider all relevant information. Obviously, thoughtful consideration of all available information is ideal. But until I get another heuristic for "should I dig into this more?" - I'm…
> But until I get another heuristic for "should I dig into this more?" Why do you need a heuristic? In what areas are you doing research where you don't have any other intuition or domain knowledge to draw on? And if you don't have that background, contextual knowledge, are you the right person to be doing the work? Are you asking the right questions?
Off-topic: Can someone recommend a good stats book for running non-deterministic simulations? Questions like how many samples to collect, what methods to fit the uncertain data etc.
Earlier quoted context omitted.
Not exactly. What I mean to say is this: We know there are certain phenotypes that predominantly appear in certain populations, in broad strokes. But while we do have lists of correlates, we don't have good definitions for what an "ethnicity" is biologically, and there is very good reason to believe no satisfactory definition exists. To use OP's example, we know that the gene mentioned is frequently found in the Inui…
I don't disagree with this, but this is very not consistent with "ethnicity is not biologically realized", which suffers from the same logical error but in the other direction. I often wonder how many entrenched culture battles could be ~resolved (at least objectively) by fixing people's cognitive variable types.
Earlier quoted context omitted.
> But until I get another heuristic for "should I dig into this more?" Why do you need a heuristic? In what areas are you doing research where you don't have any other intuition or domain knowledge to draw on? And if you don't have that background, contextual knowledge, are you the right person to be doing the work? Are you asking the right questions?
bioinformatician here. nobody has intuition or domain knowledge on all ~20,000 protein coding genes in the human body. That's just not a thing. Routinely comparing what a treatment does we do actually get 20,000 p-values. Feed that into FDR correction, filter for p then you see proof borne out that the p-value thresholding business WORKS.
Earlier quoted context omitted.
> ethnicity is not biologically realized either What does this mean? Is it contrary to what OP is saying above?
Not exactly. What I mean to say is this: We know there are certain phenotypes that predominantly appear in certain populations, in broad strokes. But while we do have lists of correlates, we don't have good definitions for what an "ethnicity" is biologically, and there is very good reason to believe no satisfactory definition exists. To use OP's example, we know that the gene mentioned is frequently found in the Inui…
Off-topic: Can someone recommend a good stats book for running non-deterministic simulations? Questions like how many samples to collect, what methods to fit the uncertain data etc.
Earlier quoted context omitted.
P-value doesn't measure interestingness. If p>0.05 there was no result at all.
Both of those statements are false. Everything has a result. And the p-value is very literally a quantified measure of how interesting a result was. That's the only thing it purports to measure. "Woman gives birth to fish" is interesting because it has a p-value of zero: under the null hypothesis ("no supernatural effects"), a woman can never give birth to a fish.