153 karma · joined November 27, 2016
This is a wrong understanding of p values. Imagine an urn with only fair 50/50 coins and taking out coins to test them. If you reject the coin being fair at 0.05<p then you will on average have 1/20 statistically significant results. All of your statistically significant results will be wrong.
Now imagine an urn that only has biased coins. Every time you reject the null hypothesis you are correct.
How many statistically significant results are actually true depend on the metaphorical urn that people draw hypothesis out of. If someone is very good at picking likely hypothesis to pan out their statistically significant results are more likely to be true.
Also if a field of study has many bad paths you could go down on then you would expect more statistically significant results to be actually wrong.
What i see here is a splitting of the set of points in the sphere? However the set of points in the sphere is not really the sphere. A point has no volume so no matter how many you add together you don't get something with a volume. This seems more akin to splitting the natural numbers into odd and even numbers which are all equally large.
The language that i see in this article and elsewhere however is suggesting that we actually duplicated the sphere (doubled the volume).
This seems incorrect.
But through that all these tasks form a big daunting mountain of stress and effort together and then i keep putting them off and procrastinate.
∇ΘJ=∇Θ(y−XΘ)T(y−XΘ)
expanded into ∇ΘJ=∇ΘyTy−(XΘ)Ty−yTXΘ+ΘT(XTX)Θ
Can someone explain why ∇Θ is only applied to the first term?
Also i have never seen gradient notation used like that before.This sounds like a dream for data analysis.
Seems really strange.