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Creating a null hypothesis does not make a hypothesis falsifiable. In order to be falsifiable it must be possible to prove the null. "No UFOs are controlled by extraterrestrials" cannot be proven.
"Remember we can never prove the null hypothesis. All we can prove is that there is a relationship or effect (H1) between two or more variables."
The danger in thinking that null hypotheses are proven wrong if rejected is twofold. Both of these dangers are related to the word prove. Although this word has different meanings in different contexts (e.g., mathematics, printing, and cooking), most dictionaries indicate that we prove something when we establish its genuineness or authenticity. Proof, therefore, leaves no room for error. If you prove something, you and others can be 100% confident that your claim is true.
The hypothesis: "God made the Universe."
The null: "God did not make the Universe."
The hypothesis is not falsifiable because it cannot be proven that God did not make the Universe.
Not only have you and Neo failed to recognize that you have tossed out 99.9999999.... % of your data
You compare to the expectation under the null hypothesis which means ALL the data has to be used.
If there were only a few (NN) cases, we would fail to reject the null hypothesis.
originally posted by: neoholographic
Before I respond and blow your post out of the water, what do you mean when you say this: ...........
originally posted by: EnPassant
The reason being that the SURROUNDING EVIDENCE renders statistical evidence irrelevant. Surrounding evidence in this case would be comprised of such things as some of the stolen goods being found in the abandoned truck, tyre marks leading up to the scene of the crime, witness testimony etc etc. The evidence completely overshadows any doubt that could arise from abstract argument about what COULD be the case. Essentially you are using the "It could be swamp gas" argument.
So tell me, what data haven't I included?
You just don't get it. You cannot reduce this hypothesis to mere statistical analysis.
In statistical inference of observed data of a scientific experiment, the null hypothesis refers to...
In statistics, statistical inference is the process of drawing conclusions from data that are subject to random variation, for example, observational errors or sampling variation.[1] More substantially, the terms statistical inference, statistical induction and inferential statistics are used to describe systems of procedures that can be used to draw conclusions from datasets arising from systems affected by random variation,[2] such as observational errors, random sampling, or random experimentation.[1] Initial requirements of such a system of procedures for inference and induction are that the system should produce reasonable answers when applied to well-defined situations and that it should be general enough to be applied across a range of situations. Inferential statistics are used to test hypotheses and make estimations using sample data.
originally posted by: BayesLikeenPas, it's obvious you don't understand still and you can't address the example either. The reason we can dispense with a formal statistical analysis when stolen goods are found is the null hypothesis. Finding stolen goods is highly significant when the null is that none exist on the truck in question. Likewise with the other evidence.
Not only have you and Neo failed to recognize that you have tossed out 99.9999999.... % of your data
originally posted by: ZetaRediculian
You are "proving" unidentified things are due to unknown things by asserting that unidentified things are identified.
Neo, just read the two examples. You have some very strange ideas about how hypotheses work and how to "prove" them. I assume you have the capacity to understand but for some reason prefer to remain blind and barefoot. I don't know if you came up with this pseudo knowledge all by yourself or you picked it up in a few popular (meaning non-academic) texts written by some whacko who doesn't have a clue but sounded convincing. However you arrived at the point at which you are today, your basic concepts are deeply flawed. Very deeply flawed.
originally posted by: EnPassant
originally posted by: ZetaRediculian
You are "proving" unidentified things are due to unknown things by asserting that unidentified things are identified.
Ergo: unidentified things cannot be identified. Nonsense.
Pulsars have been identified...
originally posted by: BayesLike
We know this is the case because we had 2 out of a billion observations with lights overhead and 2 out of a billion observations with stalls. So the combination of a stall and lights overhead should show up in somewhere around 4 times per billion-billion observations.
originally posted by: BayesLikeWe know this is the case because we had 2 out of a billion observations with lights overhead and 2 out of a billion observations with stalls.
That works
And there's data.
Radar reports - DATA
Trace evidence - DATA
Malfunctioning nukes and evading capture - DATA
Pictures and video - DATA
Eyewitness accounts from ASTUTE OBSERVERS - DATA
Close Encounters from ASTUTE OBSERVERS - DATA
These things are all data that can be used to build the ET hypothesis.
If there wasn't any DATA:
Tell me what data didn't I use? We're dealing with an aerial observed phenomena, so tell me what data didn't I use? Here's the questions again.