Folks have been seeding clouds to induce rain for over a century, but weather variability has made it hard to collect clear evidence that seeding increases rainfall. Because of this, many consider cloud-seeding to be a psuedo-science. But the latest
Cloud seeding is 1005 scientific.But its technology is based on the efficiency of its operators,topography,geographiy,meteorology,economic and political interests of the decision-makers of the given region.For instance,china works honestly to help its population whereas india works with a bias which is promoted on a large scale by high-profile ministers and officials and also scientists as they are sometimes highly ignorant and some times get huge kick-backs from the business lobby that eke out their livelihood based on the water scarcity conditions created to the detriment of the farmers and national economy and to benefit the vested interests.While the Meteorological scientists and experts are fully engaged in helping the villages and even the states to promote cloud seeding,indian government does not give a mandate to the Metweorological Department to promote cloud seeding in public interests.Even the Union ministers for Agriculture and science and technology refuse to visit countries like China,Israil,Australia or even Texas where cloud seeding is a great success to learn the basics of cloud seeding and thereby help the farmers to get more water for irrigation and the industrialists to produce inexpensive power as done in Tasmania.Scientists opposed cloud seeding in Australia ,it is said due to the limited funds under Scientific research and in the fight among scientists to cash as many funds for their research,thwey had to resort to deny funds for cloud seeding by branding it as unscientific.Officials and politicians in India get more kick backs from the tanker-water supply lobby and bribe the decision makers to put obstacles by asking them to condemn cloud seeding as unproven even without knowing what is meant by'Proven" and "unproven aspects" aspects of cloud seeding.For more details see the web sites:http://www.indiawaterportal...http://tshivajirao.blogspot...http://tshivajirao.blogspot...https://sites.google.com/si...
prof.T.Shivaji Rao,Director,centre for Environmental studies,Gitam University,visakhapatnam,india.
CLOUD SEEDING WORKS TO FIGHT GLOBAL WARMING IMPACTS---------------------------------------------------Chinese proved that cloud seeding works in the hands of patriotic experts who plan operations as per standard procedures being followed in 40 to 50 countries since 50 years.see the following web sites to get scientific proof that cloud seeding works:http://shivajirao.cloudseed...http://jcsepa.mri-jma.go.jp...http://tshivajirao.blogspot...http://gitam.edu/cos/env/En...prof.T.Shivaji Rao.B.E.,M.S.[Rice,Texas,1962]Ph.D[Hony]Director,centre for Environment,Gitam University,Visakhapatnam,India andExpert,cloud seeding project of Govt.,of A.P.state
Absence of evidence is necessarily evidence of absence. More specifically, if you could get some evidence E that would support a hypothesis H, but you get not-E instead, and you're a coherent Bayesian, then you must become at least somewhat less confident in H.
The true claim in the neighborhood of what you said is that absence of evidence isn't necessarily strong evidence of absence--absence of evidence for a hypothesis needn't make you very confident that the hypothesis is false.
Absence of evidence is sometimes only weak evidence of absence, but it's legitimate evidence nevertheless.
Good point. All evidence needs to be considered. As I have argued, Karl Popper's writing led many to see evidence exclusively in terms of either falsifying a hypothesis or failing to do so. We see this in nonsensical language like, "There was no evidence of X (p=0.2)." Sure, it's shorthand for "There was no statistically significant evidence," but it's quite misleading.
It's important to note that confidence intervals (or credible intervals) allow for shades of gray, rather than just black or white.
@Kieran: Better yet, use a hierarchical model, report the credible interval of the top level mean, and stop worrying about Type 1 error rates entirely.
This is a little off topic, but what is the point of reporting the overlap of confidence intervals? If you are testing equivalency of means, overlapping confidence intervals do not always correspond to significantly different means (unless the point estimate for one of the means is contained in the confidence interval of the other). Same goes for the multiple comparisons: a Bonferroni correction MUST be done, so why report the significance of tests at the uncorrected alpha level? That would be like saying "16+17 = 23 if we don't carry the one. If we do carry the one, 16+17=33."
Maybe there is a reason for this that I am missing?
Cloud seeding is 1005 scientific.But its technology is based on the efficiency of its operators,topography,geographiy,meteorology,economic and political interests of the decision-makers of the given region.For instance,china works honestly to help its population whereas india works with a bias which is promoted on a large scale by high-profile ministers and officials and also scientists as they are sometimes highly ignorant and some times get huge kick-backs from the business lobby that eke out their livelihood based on the water scarcity conditions created to the detriment of the farmers and national economy and to benefit the vested interests.While the Meteorological scientists and experts are fully engaged in helping the villages and even the states to promote cloud seeding,indian government does not give a mandate to the Metweorological Department to promote cloud seeding in public interests.Even the Union ministers for Agriculture and science and technology refuse to visit countries like China,Israil,Australia or even Texas where cloud seeding is a great success to learn the basics of cloud seeding and thereby help the farmers to get more water for irrigation and the industrialists to produce inexpensive power as done in Tasmania.Scientists opposed cloud seeding in Australia ,it is said due to the limited funds under Scientific research and in the fight among scientists to cash as many funds for their research,thwey had to resort to deny funds for cloud seeding by branding it as unscientific.Officials and politicians in India get more kick backs from the tanker-water supply lobby and bribe the decision makers to put obstacles by asking them to condemn cloud seeding as unproven even without knowing what is meant by'Proven" and "unproven aspects" aspects of cloud seeding.For more details see the web sites:http://www.indiawaterportal...http://tshivajirao.blogspot...http://tshivajirao.blogspot...https://sites.google.com/si...
prof.T.Shivaji Rao,Director,centre for Environmental studies,Gitam University,visakhapatnam,india.
CLOUD SEEDING WORKS TO FIGHT GLOBAL WARMING IMPACTS---------------------------------------------------Chinese proved that cloud seeding works in the hands of patriotic experts who plan operations as per standard procedures being followed in 40 to 50 countries since 50 years.see the following web sites to get scientific proof that cloud seeding works:http://shivajirao.cloudseed...http://jcsepa.mri-jma.go.jp...http://tshivajirao.blogspot...http://gitam.edu/cos/env/En...prof.T.Shivaji Rao.B.E.,M.S.[Rice,Texas,1962]Ph.D[Hony]Director,centre for Environment,Gitam University,Visakhapatnam,India andExpert,cloud seeding project of Govt.,of A.P.state
Robin,
Absence of evidence is necessarily evidence of absence. More specifically, if you could get some evidence E that would support a hypothesis H, but you get not-E instead, and you're a coherent Bayesian, then you must become at least somewhat less confident in H.
The true claim in the neighborhood of what you said is that absence of evidence isn't necessarily strong evidence of absence--absence of evidence for a hypothesis needn't make you very confident that the hypothesis is false.
It seems that for some readers I need to clarify...
So some readers think you mean what you actually write instead of the disclaimed version you obviously meant? Silly readers!
Robin is not being blunt.
Oops, I mean Robin is not necessarily being blunt.
It seems that for some readers I need to clarify that I meant that absence of evidence is not necessarily evidence of absence.
Simpleton wrote:
Absence of evidence is sometimes only weak evidence of absence, but it's legitimate evidence nevertheless.
Good point. All evidence needs to be considered. As I have argued, Karl Popper's writing led many to see evidence exclusively in terms of either falsifying a hypothesis or failing to do so. We see this in nonsensical language like, "There was no evidence of X (p=0.2)." Sure, it's shorthand for "There was no statistically significant evidence," but it's quite misleading.
It's important to note that confidence intervals (or credible intervals) allow for shades of gray, rather than just black or white.
Absence of [evidence of] anyone looking for evidence is itself evidence of absence, if we consider:
1. If nobody's looking, it suggests a consensus that there's nothing to find, which Aumann compels us to consider.
2. Publication bias means we tend not to hear about people looking unless they get positive results.
@Tim Tyler:"Absence of evidence is evidence of absence *if* some relevant tests have been performed"
...is a bit of a mouthful."
It's just not catchy enough, that's all. How about: "Absence of evidence is evidence of absence. Absence of looking for evidence is not."
@Kieran: Better yet, use a hierarchical model, report the credible interval of the top level mean, and stop worrying about Type 1 error rates entirely.
Er, when the point estimate for one of the means is contained in the confidence interval of the other means they are NOT significantly different.
This is a little off topic, but what is the point of reporting the overlap of confidence intervals? If you are testing equivalency of means, overlapping confidence intervals do not always correspond to significantly different means (unless the point estimate for one of the means is contained in the confidence interval of the other). Same goes for the multiple comparisons: a Bonferroni correction MUST be done, so why report the significance of tests at the uncorrected alpha level? That would be like saying "16+17 = 23 if we don't carry the one. If we do carry the one, 16+17=33."
Maybe there is a reason for this that I am missing?
Regarding: http://www.overcomingbias.com/2007/08/absence-of-evid.html
Neither slogan seems terribly accurate. However:
"Absence of evidence is evidence of absence *if* some relevant tests have been performed"
...is a bit of a mouthful.
> Absence of evidence is not evidence of absenceEh?
Absence of evidence is sometimes only weak evidence of absence, but it's legitimate evidence nevertheless.