Statistical significance. Not so… significant!

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In science, the success of an experiment is often determined by a measure called “statistical significance.” A result is considered to be “significant” if the difference observed in the experiment between groups (of people, plants, etc) would be very unlikely if no difference actually exists. The common cutoff for “very unlikely” is that you’d see a difference as big or bigger only 5 percent of the time if it wasn’t really there — a cutoff that might seem, at first sight, very strict.

Statistical significance has been used to draw a bright line between experimental success and failure. Achieving an experimental result with statistical significance often determines if a scientist’s paper gets published or if further research gets funded. That makes the measure far too important in deciding research priorities, statisticians say, and so it’s time to throw it in the trash.

More than 800 statisticians and scientists are calling for an end to judging studies by statistical significance in a comment published in Nature. An accompanying special issue of the American Statistician makes the manifesto crystal clear in its introduction: “‘statistically significant’ — don’t say it and don’t use it.” Science and statistics have never been so simple as to cater to convenient cutoffs. A P value, no matter how small, is just a probability. It doesn’t mean an experiment worked. And it doesn’t tell you if the difference in results between experimental groups is big or small. In fact, it doesn’t even say whether the difference is meaningful.

There is good reason to want to scrap statistical significance. But with so much research now built around the concept, it’s unclear how — or with what other measures — the scientific community could replace it. (1)

All science is based on the probability that something will happen.

But it is the improbable things which shape the cosmos.

The birth of the universe.

The death of a God.

The genesis of a human being…

People living for the wrong things.

People dying for the right ones.

Yes, it is a paradoxical world. Not a sane one.

Stop looking at the probable things.

Look at the clouds. Feel the sun behind them.

There is nothing more dull than the things which happen.

Predicting everything. Knowing it all.

The cornerstone of our civilization.

The cause of our demise.

Can you ever predict anything without knowing everything?

Can you ever know anything without already experiencing everything?

Start looking at the things which will never happen.

And right when you won’t be looking…

You will know they already did!

Chameleon theory (theories)… Everlasting worlds…

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Supercomputer simulations of galaxies have shown that Einstein’s theory of General Relativity might not be the only way to explain how gravity works or how galaxies form.

Physicists at Durham University, UK, simulated the cosmos using an alternative model for gravity — f(R)-gravity, a so called Chameleon Theory.

The resulting images produced by the simulation show that galaxies like our Milky Way could still form in the universe even with different laws of gravity.

The findings show the viability of Chameleon Theory — so called because it changes behaviour according to the environment — as an alternative to General Relativity in explaining the formation of structures in the universe. (1)

Changing theories.

The same as any other theory.

And at the end the changing theory will be accused of plasticity.

At the end, the rigid theory will be accused of dogmatism.

But why change? Why stay the same?

Why not question your own existence?!

I am the voice of silence. The destroyer of worlds.

Look at me! I explain nothing!

And yet people worship me.

Fools.

Looking for gold.

And yet during the gold rush it was not the ones seeking gold who made rich.

But those who sold shovels.

Look at what is not in the theories.

And you will discover the unchanged essence of the world.

There, between words.

Whole worlds are speaking…

Solving problems. To see there are none…

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In a future characterized by algorithms with ever increasing computational power, it becomes essential to understand the difference between human and machine intelligence. This will enable the development of hybrid-intelligence interfaces that optimally exploit the best of both worlds. By making complex research challenges available for contribution by the general public, citizen science does exactly this.

Researchers developed a versatile remote gaming interface that allowed external experts as well as hundreds of citizen scientists all over the world through multiplayer collaboration and in real time to optimize a quantum gas experiment in a lab at Aarhus University. Surprisingly, both teams quickly used the interface to dramatically improve upon the previous best solutions established after months of careful experimental optimization.

But why could players without any formal training in experimental physics manage to find surprisingly good solutions? One hint came from an interview with a top-player, a retired Italian microwave systems engineer. He said, that for him participating in the experiment reminded him a lot of his previous job as an engineer. He never attained a detailed understanding of microwave systems but instead spent years developing an intuition of how to optimize the performance of his “black-box.” In this view, the players may be performing better not because they have superior skills, but because the interface they are using makes another kind of exploration “the obvious thing to try out” compared to the traditional experimental control interface.

“The process of developing (fun) interfaces that allow experts and citizen scientists alike to view the complex research problems from different angles, may contain the key to developing future hybrid intelligence systems in which we make optimal use of human creativity” explained Jacob Sherson. (1)

Intuition.

Fun.

Silence.

These have collectively produced more science than any combination of analysis, data gathering, careful structured thought ever have during the history of mankind. Feyerabend said that the scientific method has nothing to do with what we think it has. He was right and yet, scientists think he was wrong. Because scientists today like to be called scientists. And whoever likes to impose himself is never himself…

Look at Newton.

Or Einstein.

They weren’t scientists because they said so.

But because others acknowledged them as such.

Solving problems has never solved any problem.

The key to progress is not progress per se.

The key to science is not making science.

Look at this equation. But don’t try to find a solution. Try thinking of another equation instead. And then another. And then another. Until you have nowhere else to go. Until A equals A. And then wonder.

Was there anything to solve in the first place?

Giant atoms. The same as small atoms…

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Scientists have proposed a new theory that combines some of the most mysterious phenomena in the Universe – black holes, gravitational waves, and axions – to solve one of the most confounding problems in modern physics. And it’s got experts in the field very excited.

The theory, which imagines a Universe filled with colossal ‘gravitational atoms’ that are capable of producing vast clouds of dark matter, predicts that it could be possible to detect entirely new kinds of particles using a giant gravitational wave detector called LIGO.

But what are axions? Well, they’re a bit tricky, because unlike black holes and gravitational waves, we’re not even sure if axions exist – and we’ve been searching for them for the past four decades.

Continue reading “Giant atoms. The same as small atoms…”

Science, scientific method, knowing…

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The closest and brightest supernova in decades, SN 2014, brightens faster than expected for Type Ia supernovae, the exploding stars used to measure cosmic distances, according to astronomers. Another recent supernova also brightened faster than expected, suggesting that there is unsuspected new physics going on inside these exploding stars. (1)

We expect things to happen.

And when they don’t, we simply change the things we expect.

We can never be wrong.

Science will always work on this tautological basis.

But the point is not to always adapt so that you are right.

The point is to know.

And what “is” we already know.

We live it.

We experience it.

All we have to do is accept it…

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