3 ms·

>"You can use Student’s t-distribution to compute an upper bound for the probability of T under the assumptions" I'm not sure what you are arguing anymore. I a

by nonbel 9y ago

>"You can use Student’s t-distribution to compute an upper bound for the probability of T under the assumptions" I'm not sure what you are arguing anymore. I am saying you will never test a parameter value in isolation, it is always part of a model with other assumptions. There is simply no such thing as testing a parameter value alone. To define a likelihood you need more than simply a parameter... You seemed to be disagreeing with that, but are now acknowledging the presence of the other assumptions.

tprice7 9y ago
“I'm not sure what you are arguing anymore.” It’s the claim I make in 3, and then the secondary claim that making our upper bound on p(null | T, normal, iid) small for significant p-values (i.e. p(T | null, normal, id)) could be used as a criterion for whether our threshold for statistical significance is small enough. “You seemed to be disagreeing with that” I’m not sure what I said that gave that impression. I didn’t mention anything about the normal / iid assumptions initially not because I thought we weren’t making these assumptions but because I didn’t think these details were essential to my point.