[quote name='SteveDeFacto' timestamp='1330076958' post='4916153']
I could argue that for standardized testing the cultural influence in the UK on males is actually much greater than the cultural influence in the US on females or that the link which was posted is not very scholarly. However, I would much rather prove that there is a biological basis for the differences in math ability between the genders.

Correlation doesn't imply causation.

And in particular, there is a correlation between the finger length thing, and one's sex, so this is equivalent to simply restating that there's a correlation between sex and SAT scores, which again tells us nothing about what the reason for that is.

Are you seriously tell us that your argument is: "There is a correlation between sex and blah, and there are biological differences between the sexes, therefore blah has a biological cause"?

The same argument works for the UK exam results anyway - there would be a correlation between the finger length thing, and exam results. According to you, that means that girls being better than boys at maths must have a biological basis. (Well, unless you're suggesting that for some reason in the UK, the finger length thing doesn't apply, because for some reason boys in the womb now get less testerone than girls ...)
[/quote]

Seriously, how can you be this dense?! What more evidence could I possibly provide to prove men are generally better at mathematics?

Do you even know what "Correlation doesn't imply causation." means?

Do you even know what "Correlation doesn't imply causation." means?

Correlation is a tool. A hammer by itself isn't all that useful. It certainly won't build a house all by itself. But have you ever tried to build a house without a hammer?

Correlation is a tool.

Have you ever tried to pound a hammer into a wall with nails?

[quote name='SteveDeFacto' timestamp='1330095898' post='4916217']
Correlation is a tool.

Have you ever tried to pound a hammer into a wall with nails?
[/quote]

What is that even suppose to mean?! Yes, yes I have tried to pound a hammer into a wall with nails... I was successful in my attempt...

From the wikipedia page on correlation fallacy:

In a widely-studied example, numerous epidemiological studies showed that women who were taking combined hormone replacement therapy (HRT) also had a lower-than-average incidence of coronary heart disease (CHD), leading doctors to propose that HRT was protective against CHD. But randomized controlled trials showed that HRT caused a small but statistically significant increase in risk of CHD. Re-analysis of the data from the epidemiological studies showed that women undertaking HRT were more likely to be from higher socio-economic groups (ABC1), with better than average diet and exercise regimens. The use of HRT and decreased incidence of coronary heart disease were coincident effects of a common cause (i.e. the benefits associated with a higher socioeconomic status), rather than cause and effect as had been supposed.[/quote]
This is pretty much word-for-word the trap you are falling into. You keep presenting research that shows a correlation between two variables, and assuming that is 'proof'.

Unfortunately, a correlation between two variables is completely meaningless. The actual relation may have nothing to do with the two variables you are studying, and is most of the time due to accidental selection bias when picking the research subjects.

What is that even suppose to mean?! Yes, yes I have tried to pound a hammer into a wall with nails... I was successful in my attempt...

Well it appears that has caused you severe logical trauma.

edit: HOW ABOUT PHILOSOPHY GUYS HUH? philosophy sure is great. /discuss

From the wikipedia page on correlation fallacy:

In a widely-studied example, numerous epidemiological studies showed that women who were taking combined hormone replacement therapy (HRT) also had a lower-than-average incidence of coronary heart disease (CHD), leading doctors to propose that HRT was protective against CHD. But randomized controlled trials showed that HRT caused a small but statistically significant increase in risk of CHD. Re-analysis of the data from the epidemiological studies showed that women undertaking HRT were more likely to be from higher socio-economic groups (ABC1), with better than average diet and exercise regimens. The use of HRT and decreased incidence of coronary heart disease were coincident effects of a common cause (i.e. the benefits associated with a higher socioeconomic status), rather than cause and effect as had been supposed.

This is pretty much word-for-word the trap you are falling into. You keep presenting research that shows a correlation between two variables, and assuming that is 'proof'.

Unfortunately, a correlation between two variables is completely meaningless. The actual relation may have nothing to do with the two variables you are studying, and is most of the time due to accidental selection bias when picking the research subjects.
[/quote]

Yes, I understand that very well. However, you don't seem to understand that the only thing that exists are correlations! If I were to test my theory any results will be correlation! Facts are only theories with extremely strong correlations.

[quote name='swiftcoder' timestamp='1330096529' post='4916224']
From the wikipedia page on correlation fallacy:

In a widely-studied example, numerous epidemiological studies showed that women who were taking combined hormone replacement therapy (HRT) also had a lower-than-average incidence of coronary heart disease (CHD), leading doctors to propose that HRT was protective against CHD. But randomized controlled trials showed that HRT caused a small but statistically significant increase in risk of CHD. Re-analysis of the data from the epidemiological studies showed that women undertaking HRT were more likely to be from higher socio-economic groups (ABC1), with better than average diet and exercise regimens. The use of HRT and decreased incidence of coronary heart disease were coincident effects of a common cause (i.e. the benefits associated with a higher socioeconomic status), rather than cause and effect as had been supposed.

This is pretty much word-for-word the trap you are falling into. You keep presenting research that shows a correlation between two variables, and assuming that is 'proof'.

Unfortunately, a correlation between two variables is completely meaningless. The actual relation may have nothing to do with the two variables you are studying, and is most of the time due to accidental selection bias when picking the research subjects.
[/quote]

Yes, I understand that very well. However, you don't seem to understand that the only thing that exists are correlations! If I were to test my theory any results will be correlation! Facts are only theories with extremely strong correlations.
[/quote]

False. Rain is caused by clouds.

However, you don't seem to understand that the only thing that exists are correlation! If I were to test my theory any results will be correlation! Facts are only theories with extremely strong correlations.

Nope. Correlation indicates the possibility of causation. However, one still has to prove the causation before it becomes 'fact'.

If you can't explicitly find and test the actual chemical/biological/genetic process by which the causation occurs, then you are forced to fall back to proof by exhaustion. In other words, you have to show a counter-correlation against every other possibility that could account for the phenomenon.