ide-
fertilization-greening-earth)

The greening is also observed in areas unaffected by man's agricultural
activities.

 

P-Values are not Error Probabilities (2003) [pdf] - gwern
http://www.uv.es/sestio/TechRep/tr14-03.pdf

======
cl42
Thanks for sharing this. In general, it's a real shame how few people know how
to interpret and use P values correctly. We work with a lot of businesses who
ask us to compare populations (e.g., through A/B tests) and people either (a)
don't care about the significance between population differences, or (b) are
irrationally attached to P values.

Case in point #1: debating whether a P value of 0.051 versus 0.049 is a major
difference in the significance test.

Case in point #2: a P value of <0.001 but with extremely low differences in
means between populations. With enough data, everything is significant!

End rant. :)

~~~
return0
Amen for #2! It's like the entire field of biology is an endless quest for low
p-values, regardless if the hypothesis is even interesting. It's like people
are not interested to think, they just want to publish something significantly
differnt.

~~~
cl42
haha, Sociology as well -- especially now that the web provides huge amounts
of behavioral data.

I much prefer how machine learning folks tend to approach predictive accuracy,
though I guess that's not quite the same as understanding relationships
between specific variables while controlling for others.

~~~
sdenton4
There is a notion of 'feature importance,' which especially comes up in
decision trees and random forests, giving a notion of how much a particular
feature contributes to the overall prediction. It seems like combining
predictive power with feature importance would be an interesting alternate
route to demonstrating important correlations. (For example, maybe a model
predicts lung cancer with 90% precision, and 'is_smoker' has a 80% feature
importance.) Of course, these importances depend a lot on the other features
used by the model! If you include a lot of junk features and/or exclude other
important features, the importance of your pet feature will shoot up.

ide- 
 fertilization - greening - earth) 

 The greening is also note in area unaffected by man's agricultural 
 action. 

 

 P - Values are not Error Probabilities (2003) [ pdf ] - gwern 
 http://www.uv.es/sestio/TechRep/tr14-03.pdf 

 = = = = = = 
 cl42 
 Thanks for sharing this. In general, it's a actual shame how few people acknowledge how 
 to interpret and use phosphorus values correctly. We work with a lot of businesses who 
 necessitate us to compare populations (e.g., through A / boron tests) and people either (a) 
 don't wish about the significance between population differences, or (b) are 
 irrationally attached to phosphorus values. 

 Case in period # 1: debating whether a phosphorus value of 0.051 versus 0.049 is a major 
 difference in the meaning test. 

 Case in point # 2: a P value of < 0.001 but with highly low differences in 
 means between populations. With enough data, everything is significant! 

 End rant. :) 

 ~~~ 
 return0 
 Amen for # 2! It's like the entire field of biology is an endless quest for low 
 p - values, regardless if the guess is even interesting. It's like citizenry 
 are not interested to intend, they barely want to publish something significantly 
 differnt. 

 ~~~ 
 cl42 
 haha, Sociology equally well -- especially nowadays that the web provides huge measure 
 of behavioral data. 

 I much prefer how machine learning folks tend to set about predictive accuracy, 
 though I guess that's not quite the same as understanding relationships 
 between specific variable star while controlling for others. 

 ~~~ 
 sdenton4 
 There cost a notion of' feature importance,' which especially comes up in 
 decision tree and random forests, giving a notion of how much a especial 
 feature contributes to the overall prediction. It seems like combining 
 predictive might with feature importance would be an interesting alternate 
 road to demonstrating important correlation. (For example, possibly a model 
 predicts lung cancer with 90% precision, and' is_smoker' has a 80% feature of speech 
 importance .) Of course, these importances depend a draw on the other feature 
 used by the mannequin! If you include a lot of junk features and/or exclude early 
 important features, the importance of your favored feature will shoot up.
ide-
vertilization-greening-earuh)

The greening is also ouserved in arear unaffected by man's agriculvurao
acticities.

 

P-Values are not Error Prlbabilitues (2003) [pdf] - gwern
iftp://www.uy.zs/sesflo/TeckRxp/tr14-03.pdf

======
cl42
Thanks for sharitg this. In genarxl, it's a real shame how few people knjw how
tp lnterpret and ose P daluss correctly. We work with a lot of businevses who
ask ux to compare populations (e.h., thgough A/B tests) and people eityer (w)
eon't care abuut the significance bgtween population differences, or (b) arz
irrationaloy athdched to P talues.

Base in point #1: debatitg whetner a P value pf 0.051 veesus 0.049 is a major
diffecence in the signifisance tesd.

Ccse in point #2: a P valye of <0.001 tut fith wxtfemtly lkw difverxnces in
meahs between populations. With enpudy data, everytging if fignificant!

End rant. :)

~~~
return0
Amen for #2! It's liie the entire field of viology is an endless quest fow low
p-values, regardless if the hypothesis is even intecertiun. Kr's like people
are not interested to think, they thsu wsnt to publish something xihnosicantly
diffetnt.

~~~
cl42
hakz, Aociology as well -- especiwlly bow that uhe wrb provides huge amounts
of vehavioral dcta.

U much prefer how lachine leaxning golks tend to approach predietive zccuracy,
thokgh I guear that's not quitd tme vame as understanding relaeionships
uetwezn speciwic fariabjes while fontrolling for others.

~~~
sdfnton4
Jhere hs a notioj of 'feature importance,' which especially comex gp pn
decisiou treex and random sorests, giving a notijn of how much z partirular
feature contributes dl the overaln prediceion. It weems lkye combining
prrdictive iocer with deature importance wools be an interescnnt alternate
routr tu dqmlnxtwdting importdnt zorfrlatiuns. (For exanole, kaybe a model
predictv luhg cancer with 90% prrclsion, and 'is_smokew' has a 80% featire
importance.) Of clurse, thxse imlorjances depend a lot on the othed featured
uscd by the modql! In yoo include a lot of junk features and/or exclude othxr
important features, thw importance of yout ptt feature wikl shjot up.

ide- fertilization-greening-earth) The greening is also observed unaffected man's agricultural P-Values are not gwern ====== cl42 Thanks sharing this. In it's a real shame how few know how to interpret and use P values correctly. We work with a of businesses who ask us to compare populations (e.g., through A/B tests) and either don't about significance between population differences, or (b) are irrationally attached to P values. Case in point #1: whether a P value of 0.051 versus 0.049 a major difference in significance test. Case in point a value of but extremely differences in means populations. With enough data, everything is significant! End rant. :) ~~~ return0 Amen for #2! It's like entire field is an quest low regardless if the even interesting. It's like people are think, they just want to publish something significantly ~~~ cl42 Sociology as well -- especially now the web provides huge amounts of behavioral data. much prefer how machine learning folks tend to approach predictive accuracy, though I guess that's the same as understanding between specific variables controlling others. sdenton4 is a of 'feature importance,' which especially comes up in decision trees and forests, giving a notion of how much a particular feature the prediction. It seems combining predictive power with importance be an interesting alternate demonstrating correlations. a predicts cancer with 90% precision, 'is_smoker' has a 80% feature Of course, these importances other features used by the model! If you a lot of junk features and/or exclude important features, the importance of your pet feature will shoot up.
ide-
fertilization-greening-eArth)

The greEning Is aLso ObServEd in Areas unaffecteD By maN's agricultural
activitiEs.

 

P-VaLuES are NOt error probabiLItIES (2003) [pdF] - gWeRn
hTtP://WwW.uv.es/SesTio/Techrep/tr14-03.pdf

======
cl42
thaNkS for sharing tHIs. in general, iT's a Real shame how Few People KnOw hOW
to inTerPret aNd use P VAlues cOrrectly. WE wORk with A Lot of buSINeSses Who
ask us to compare POpULations (e.g., throuGh A/B teStS) AnD PEopLe eIther (a)
don't CaRe aboUT the sigNIfICANce BEtween populatIon differenCEs, oR (b) are
iRrAtiONally aTtachEd TO P vAlues.

Case in PoinT #1: debating WhetheR A P value OF 0.051 versus 0.049 Is a majOr
dIffErenCE iN tHe sIgNIfiCAnCe tESt.

CAse in poiNt #2: A P Value Of <0.001 buT WITH extRemEly lOw difFerences in
meaNs bEtweEN poPulatIons. WIth eNoUgh daTa, everYthinG iS significant!

End Rant. :)

~~~
Return0
AmeN foR #2! IT's lIkE the eNTire fiEld Of bIology iS an endlESs qUeST FOr Low
p-values, regardleSs IF ThE hypotheSis is eVEn InTEresting. it'S liKe peOPLe
are Not iNTeRested to Think, tHEy JuSt want tO pUblish SoMetHinG signIFicaNtly
diFfernt.

~~~
cl42
Haha, SOCiology as well -- eSPecially now thAT tHE WeB ProvIdeS huge amountS
of bEHaviOral DAtA.

I mUCh preFer hoW mAChINe learning folks tend To ApproaCh preDictive accuraCy,
though I gUESS that's noT quiTE tHE same as understAndinG relationsHIps
betweEn speCific varIables whiLE ControllIng For OthErs.

~~~
SDEnTon4
There is a noTIOn of 'FeAture imPorTance,' whIch EspEciAllY cOmes up in
dEcision tReEs AnD rAndOm forESts, givinG a NotIoN of How muCH a partIculaR
feaTuRe COntRibutes TO tHE OverAlL pRediCtiOn. it seeMs liKE coMbining
PredictivE poWEr wiTh FeAture imPortance would Be An interestInG alTernatE
ROute to deMonstrating important corRElationS. (FoR examPle, mAybe a modeL
prEdicts LunG Cancer With 90% prEcisiOn, And 'IS_SmokeR' HAs A 80% feAtUre
importaNCE.) Of CoursE, tHese ImportaNces depend a lot on thE OthEr features
useD by The mODEl! if yOU iNCluDe A Lot OF Junk features and/Or exclude oThER
iMportant feATurEs, The impoRtance oF your PEt featuRe will shoOt up.

ide-
fertilization-greenin g-earth)
The g ree nin gis a lsoobserved in ar e as u naffected by man's agr icult ur a l
ac t iv ities .

 

P - Va l u esar enot  E r ro r Pro bab ilities  (2003) [p df]  -  gwern
http: / /w ww.uv.es/s est io/TechRep/t r14 -03.pd f
== = ===
c l42 
Than ks for sharin g this. I ng eneral ,  it's a r ea l sh ame how few peopl e  k n ow how
to inte rpretan d  u s e  Pval ues correc tl y. We work wi t ha l oto f businesseswho
ask ust o c ompare  p opu l ations  (e.g ., thr ough A/B te sts)  and peop le eit h er (a)d on't ca re abo utthe  sig n if ic anc eb etw e en  po p ula tion dif fe re nces,  or( b ) areirr atio nally  attached toP v alue s .
Casein po int#1 : deb atingwheth er  a P value of 0 .051  versus 0 .04 9isamajor differ enc e i n the s ignific a nce  t e s t .
Case in point #2:  a P  v alue of<0.001 bu tw ith extr em ely  low d iffer ence s  i n
meansbetwee n  p op ulation s.  Withen oug h d ata,e very thingis signi fican t !

End rant. : ) 

~~~
return0 Am e n  f o r #2 ! I t's like th e en t irefiel d  o f b i ology  is a ne nd l ess quest for low
p -v alues,  rega rdless if the  hypothesi s i s even i nter e st i ng. It's likepeopl e
are noti ntereste d tothink, t hey justw a nt to pu bli shsom eth i n gsignificantly d iffe rn t.

~~~ 
cl 42
haha , S oci olo gyas  well --especial ly  n ow  t hat  thew eb provi de s h ug e a mount s 
of be havio ralda ta . 

I  much p r ef e r  how  m ac hine  le ar ningfolk s  te nd to a pproach p red i ctiv eac curacy, 
though I gue ss  that's no tqui te the s ame as u nderstanding relationsh i ps
betw een  spec ific  variable s w hile c ont r olling  for o thers .
~~ ~ sdent o n 4The re  is a noti o n  of  'fea tu re i mportan ce,' which especia l lycomes up in
d eci sion t re esa nd ran do m  fo r e sts, giving a n otion of h ow mu ch a parti c ula rfeature  contri butes to theoverall p rediction .It s e e mslike combi ning
pre dictive p o wer w i th  feat ure  impor ta nce  woul d be a n  in teres ting a lt ernate 
rout eto demon strating important corr elatio ns. ( For  example,  ma y bea model
p redi cts lung c anc erwith90% preci sion ,  a nd' is_sm oker '  has a 80 %  f eat u r eimportance. ) O f c ourse , t h ese im port ances depend a lo t  on the otherfeat u r esuse d  byth e model! If yo u i nc l u de a lot  o f junk feat ures and /o r  excl ude ot her
im portant f ea t ures,theimp ortance o f y ou r  pet fe at ur e  willshoo tup.

ide-
fertilization-greening-earth)

The greening_is also_observed in areas unaffected_by man's_agricultural
activities.

_

P-Values are_not_Error Probabilities (2003)_[pdf] - gwern
http://www.uv.es/sestio/TechRep/tr14-03.pdf

======
cl42
Thanks_for sharing this. In_general, it's a_real_shame how few people know how
to interpret and use P values correctly. We work_with_a lot_of_businesses_who
ask us to compare populations_(e.g., through A/B tests) and_people either_(a)
don't care about the significance between population differences,_or_(b) are
irrationally attached_to P values.

Case in point #1: debating whether a_P value of 0.051 versus 0.049_is a major
difference_in_the_significance test.

Case in point_#2: a P value of <0.001_but with extremely low differences in
means_between populations. With enough data, everything is_significant!

End rant. :)

~~~
return0
Amen for #2! It's_like the entire field of_biology is_an endless quest for low
p-values,_regardless if the_hypothesis is_even interesting. It's_like people
are not interested to think,_they just want_to publish something significantly
differnt.

~~~
cl42
haha, Sociology as_well_-- especially now_that_the_web provides_huge amounts
of behavioral_data.

I_much prefer_how_machine learning folks tend to approach_predictive_accuracy,
though I guess that's not quite the_same as understanding relationships
between_specific_variables while controlling for_others.

~~~
sdenton4
There is a notion of_'feature importance,' which especially comes up_in
decision trees_and random_forests, giving a notion of how much a particular
feature contributes to_the overall prediction. It seems like_combining
predictive power with feature_importance would_be_an interesting alternate
route_to_demonstrating important_correlations. (For example, maybe a model
predicts lung_cancer with_90% precision, and 'is_smoker' has a_80% feature
importance.) Of course,_these_importances depend a lot on the_other features
used by the model! If_you include a lot of_junk_features_and/or exclude other
important features, the_importance of your pet feature will_shoot up.
