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Just Plain Wrong

That's not a question for a definition, it's how a circumstance fits a definition. A definition would be in a question like, say "define normal."

I dont need you to define my questions...Im capable of asking my own...
 
No, you have NOT answered my question... even in THIS thread you contradicted yourself. You defined normal in statistical terms, then denied that being Jewish was not normal. You need to identify a definition of normal that applies, universally and contextually, as definitions do. THEN I will answer your question.

Define normal.

CC your typical debate style of no substance and bullying and bogarting does NOT work with me...your twisting and fabricating others intent and meaning will not work either....you will NOT win this debate asking a perpetual question ad nausem. Ive answered your question several times...your twisting my response to fit your need to avoid answering mine is typical of your avoidance tactics..so again
Define how homosexuality is Normal.
 
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Probably a dumb question but what constitutes normal, statistically? Within two standard deviations?

+/- one standard deviation from the mean on a normal curve includes 68% of the population. Once you get past one SD from the mean you are firmly in the realm of abnormal, since you are either in the top or bottom 16% with said characteristic.
 
+/- one standard deviation from the mean on a normal curve includes 68% of the population. Once you get past one SD from the mean you are firmly in the realm of abnormal, since you are either in the top or bottom 16% with said characteristic.

Actually, according to my Probability and Stats class, if the data falls within two standard deviations, or within 95%, it is normal.
 
+/- one standard deviation from the mean on a normal curve includes 68% of the population. Once you get past one SD from the mean you are firmly in the realm of abnormal, since you are either in the top or bottom 16% with said characteristic.

What about a skewed curve?

sexuality.jpg
 
A one third occurance is not normal?

I think Evan is correct.
 
Actually, according to my Probability and Stats class, if the data falls within two standard deviations, or within 95%, it is normal.

Ultimately, it's an arbitrary point where "normality" is defined.

Using the method you described, color-blindness qualifies as "normal" since the colorblind population exceeds 5% of the total population. But color-blindness is something most people would consider an abnormality.
 
Ultimately, it's an arbitrary point where "normality" is defined.

But CC said it is defined in stat. Was he trying to hide a value judgement in math?

Using the method you described, color-blindness qualifies as "normal" since the colorblind population exceeds 5% of the total population. But color-blindness is something most people would consider an abnormality.

Total color blindness is not 1/20. Partial (especially minor) color blindness is probably statistically significant (5%) and this normal.
 
Partial (especially minor) color blindness is probably statistically significant (5%) and this normal.

The part in bold makes absolutely no sense. What study are you referring to?

(Seriously, if you don't know what a term means, don't use it to pretend to know what you are talking about.)
 
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A normal curve is a two-tailed distribution. It's not a 1/3rd occurrence because the third encompasses polar opposites. It's actually two types of 1/6th occurrences.

Sure it is. If we take one SD, what is the percentage chance of a 'non-normal' occurence? 33%

The part in bold makes absolutely no sense. What study are you referring to?

(Seriously, if you don't know what a term means, don't use it to pretend to know what you are talking about.)

5% = statistically significant. Look it up. Don't confuse it with p-value.

Choosing level of significance is a somewhat arbitrary task, but for many applications, a level of 5% is chosen, for no better reason than that it is conventional.
http://en.wikipedia.org/wiki/Statistical_significance

See?
 
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Sure it is. if we take one SD, what is the percentage chance of a 'non-normal' occurance? 33%

The high and low ends of the curve are not equivalent to each other, so they can't be lumped together as a single "occurrence".



5% = statistically significant. Look it up. Don't confuse it with p-value.

False.

How about a wager. Why don't you look it up and then post some solid academic evidence that "statistically significant" means "5% of a population". If you can actually find that mythical thing, I'll donate $100 to the forum in your name. If you can't find this mythical thing, all you have to do is admit your error.

Deal?
 
Have you taken a statistics class before? Depending on the subject of what you're testing, p < 1%, p < 5%, or p < 10% could all qualify as statistically significant. Anything over 10% is statistically insignificant, because the chances of the event happening by chance are far too great to be deemed different than than the control.

That's statistics. I'm not sure if a statistics definition is what you should be looking for in a homosexuality debate, though... I thought the fact that it happens naturally across virtually all species in the animal kingdom, not just humans, was pretty strong evidence of homosexuality's normality. People should keep in mind that "rare" and "abnormal" are not the same thing.

It is possible for something to be both normal and rare.
 
Ultimately, it's an arbitrary point where "normality" is defined.

Using the method you described, color-blindness qualifies as "normal" since the colorblind population exceeds 5% of the total population. But color-blindness is something most people would consider an abnormality.

In this case, it's less about numbers and more about perception.
 
The high and low ends of the curve are not equivalent to each other, so they can't be lumped together as a single "occurrence".





False.

How about a wager. Why don't you look it up and then post some solid academic evidence that "statistically significant" means "5% of a population". If you can actually find that mythical thing, I'll donate $100 to the forum in your name. If you can't find this mythical thing, all you have to do is admit your error.

Deal?


Oh and this is from Wikipedia. I could have just as easily pulled it from a stat textbook if I still had one on me. I suppose I should have let him look it up himself...but whatever. You probably weren't going to donate the 100 bucks anyways.

"The significance level is usually denoted by the Greek symbol α (lowercase alpha). Popular levels of significance are 10% (0.1), 5% (0.05), 1% (0.01), 0.5% (0.005), and 0.1% (0.001). If a test of significance gives a p-value lower than the significance level α, the null hypothesis is rejected. Such results are informally referred to as 'statistically significant'. For example, if someone argues that "there's only one chance in a thousand this could have happened by coincidence," a 0.001 level of statistical significance is being implied. The lower the significance level, the stronger the evidence required. Choosing level of significance is a somewhat arbitrary task, but for many applications, a level of 5% is chosen, for no better reason than that it is conventional.[3][4]"
 
Statistics courses are for suckas. :lol:

They will give you a formula for figuring out the likelihood of a coin coming up heads after coming up tails 99 times in a row. That formula doesn't tell you the truth. The truth is it is still 50/50 heads/tails. The outcome is not dependent upon previous coin flips.
 
The high and low ends of the curve are not equivalent to each other, so they can't be lumped together as a single "occurrence".

To explain this more, look at things in terms of IQ. 84% of people have IQ's above 85.

Conversely, 84% of people have IQ's below 115.

When a distribution is two-tailed, the people on the other side of the distribution are included in the comparison of a group to the rest of the population. That's why calling it a 32% "occurrence" is inaccurate. By doing that, you fail to recognize that about 16% of that 32% is actually included in the comparison for the remaining 16%.

Genital herpes infections are an example of something that has a rate of about 16% that people pretty much universally consider abnormal (i.e. having herpes is abnormal). 84% of people do not have herpes.

But ultimately, as I said earlier, the "cut-off" for what qualifies as normal is basically an arbitrary thing. If one feels that having herpes is abnormal, then one must conclude that IQ's above 115 or below 85 are also abnormal. If one doesn't feel that an IQ above 115 or below 85 is abnormal, then they also have to conclude that having herpes is also not abnormal.
 
Oh and this is from Wikipedia. I could have just as easily pulled it from a stat textbook if I still had one on me. I suppose I should have let him look it up himself...but whatever. You probably weren't going to donate the 100 bucks anyways.

"The significance level is usually denoted by the Greek symbol α (lowercase alpha). Popular levels of significance are 10% (0.1), 5% (0.05), 1% (0.01), 0.5% (0.005), and 0.1% (0.001). If a test of significance gives a p-value lower than the significance level α, the null hypothesis is rejected. Such results are informally referred to as 'statistically significant'. For example, if someone argues that "there's only one chance in a thousand this could have happened by coincidence," a 0.001 level of statistical significance is being implied. The lower the significance level, the stronger the evidence required. Choosing level of significance is a somewhat arbitrary task, but for many applications, a level of 5% is chosen, for no better reason than that it is conventional.[3][4]"

:lol: That's hilarious.

Now, since you apparently have chosen not to comprehend what you quoted, where do you see anything that relates to population in that quote? (I'll give you a hint: it's not there).
 
Having herpes is normal. Over 25% of Americans have it. The only difference is: people want to believe it is abnormal. It's not about statistics. It's nearly always perception.

"Normal" is always perception. Even with statistics. That's th epoint I was making about the arbitrary cut-off ranges.
 
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