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Quite frankly, because of your inexperience with analyzing data, you totally misunderstood his statement. You do have to use all of the data in the sample, you can't pick and choose to look at only the data you like. What you are doing is ignoring 99.999999999% of the data and selecting a few cases which you like. That isn't permitted.
The Null is ASSUMED to be true. YOU DON'T TEST THE NULL. The only thing you can do is demonstrate the null is false. This is why it's called falsification Horshack.
Let's look at the heart attack and aspirin example again. The alternative hypothesis is looking for a relationship between aspirin and the prevention of heart attacks. The whole idea behind falsification is to put the null in a position of strength and it's assumed true.
You then give people Aspirin and see if there's a correlation between taking aspirin and preventing heart attacks.
For instance, a certain drug may reduce the chance of having a heart attack. Possible null hypotheses are "this drug does not reduce the chances of having a heart attack" or "this drug has no effect on the chances of having a heart attack". The test of the hypothesis consists of administering the drug to half of the people in a study group as a controlled experiment. If the data show a statistically significant change in the people receiving the drug, the null hypothesis is rejected.
For instance, a certain drug may reduce the chance of having a heart attack. Possible null hypotheses are "this drug does not reduce the chances of having a heart attack" or "this drug has no effect on the chances of having a heart attack". The test of the hypothesis consists of administering the drug to half of the people in a study group as a controlled experiment. If the data show a statistically significant change in the people receiving the drug, the null hypothesis is rejected.
neoholographic
First let me say, you don't test the null hypothesis. The null is assumed to be true. Here's an example:
ZetaRediculian
reply to post by usertwelve
This is the guts of it. The null hypothesis is a statistics problem, not a scientific method problem. The statistical evidence weighs in favor of the ETH which falsifies the null hypothesis. I suppose it comes down to a matter of individual taste.
Its only a statistical problem if there is actual statistics to show. I haven't seen any and nothing has been defined. "Statistical evidence" needs to be in a statistical format. Bluebook was an attempt at that. Showing YouTube videos and links to ufo websites is not statistical information.
neoholographic
Tell me, exactly what 99.99999999% of the data am I ignoring. Could you point me to this data?edit on 16-4-2014 by neoholographic because: (no reason given)
EnPassant
Statistical in this sense means that, as data comes in, a picture is builds up. Myriad pieces of data create a statistical drift towards ETH.edit on 16-4-2014 by EnPassant because: (no reason given)
EnPassant
It is not a matter of screening out a few events. It is a matter of identifying emergent themes and backing up good sightings with evidence from other domains. The themes in question are stalling car engines, falling leaf motion etc. there are enough sightings of this nature to formulate the hypothesis.
Phage
reply to post by EnPassant
No. What you are talking about would be invalidation of evidence in favor of the hypothesis. A hypothesis is not falsified by invalidation of evidence. A hypothesis can be weakened by invalidation of evidence but it is not falsified by it.
To falsify it only requires a significant falsification of the EVIDENCE or an alternative explanation for that evidence.
Falsification of a hypothesis is carried out by verification of the null hypothesis. Falsification is carried out by obtaining evidence which validates the null. The OP states that the null is: "No UFOs are controlled by extraterrestrials." This is not a falsifiable hypothesis.
edit on 4/15/2014 by Phage because: (no reason given)
BayesLike
EnPassant
It is not a matter of screening out a few events. It is a matter of identifying emergent themes and backing up good sightings with evidence from other domains. The themes in question are stalling car engines, falling leaf motion etc. there are enough sightings of this nature to formulate the hypothesis.
Car engines fail without UFOs. How often is that under different conditions -- and in different parts of the country / city/ etc. So you have some cases where somebody saw a light and their car failed. So what? Is that frequency of cases within the normal probability of occurence or not? This is how you have to work with this data. Car engines do fail with or without lights being spotted. A few cases? Not so impressive as I'd expect some just considering how often car engines do fail. Millions of cases in a particular week and only when lights are spotted -- now that would be impressive (rare) under the null hypothesis!
EnPassant
Ok. The null should be; The ETH is not based on a reasonable appraisal of the evidence. The hypothesis is that ETH is a reasonable assumption, considering the evidence.
neoholographic
reply to post by ZetaRediculian
Wow, just wow.
It's sad when people can't even accept something so simple to grasp. You said:
For instance, a certain drug may reduce the chance of having a heart attack. Possible null hypotheses are "this drug does not reduce the chances of having a heart attack" or "this drug has no effect on the chances of having a heart attack". The test of the hypothesis consists of administering the drug to half of the people in a study group as a controlled experiment. If the data show a statistically significant change in the people receiving the drug, the null hypothesis is rejected.
You're not testing the null, you're testing the hypothesis that a certain drug may reduce the chance of a heart attack.
Why? Because UFOs are unidentifieds -- and the null involves identifieds.
It's OK, and preferable in all cases, to work with samples. But we don't have samples, we just have selected interesting observations which are an extremely small fraction of all observations.
Tell me, exactly what 99.99999999% of the data am I ignoring. Could you point me to this data?
1
: having no legal or binding force : invalid
2
: amounting to nothing : nil
3
: having no value : insignificant
neoholographic
reply to post by ZetaRediculian
Zeta, you're making yourself look bad if you will not accept or even try to understand a simple scientific method of ensuring that those who support the alternative hypothesis have to falsify the null which is assumed to be true.