RichardKennaway comments on The usefulness of correlations - Less Wrong

13 Post author: RichardKennaway 04 August 2009 07:00PM

You are viewing a comment permalink. View the original post to see all comments and the full post content.

Article Navigation

Comments (52)

Comments (52)

You are viewing a single comment's thread. Show more comments above.

Comment author: RichardKennaway 11 August 2009 04:13:49PM *  1 point [-]

But social science doesn't respect a correlation of .6 because they think it's a good way to measure something that could be measured directly. They find correlations either as an important step in establishing causation, a way to get large-scale trends, or a good way to measure something that can't be measured directly.

A correlation of 0.6 is a bad measurement, period. It does not become a good one for want of a better.

Can you or Richard give an example of something the people investigating lung cancer could have done with direct measurement that would have been more productive than analyzing the cigarettes-smoking correlation?

I don't know what you mean by "analysing" a correlation, but this is some of what they did do.

I could have mentioned epidemiology in my intro. The reason it depends on statistics is that it is often much more difficult to discern the actual mechanism of a disease process than to do statistical studies. Googling turns up this study which is claimed (by the scientist doing the work) to be the very first demonstration of a causal link between smoking and lung cancer -- in April of this year (and not the 1st of the month).

But the correlations remain what they are, and it still takes a lot of work to get somewhere with them.

Comment author: Vladimir_Nesov 11 August 2009 04:22:16PM 1 point [-]

A bad measurement can still be the best there is.

Comment author: Douglas_Knight 11 August 2009 10:39:32PM *  0 points [-]

A correlation of 0.6 is a bad measurement, period. It does not become a good one for want of a better.

But it is useful. I think Yvain asked the wrong question. You can do better than correlations, but do you deny that you can draw from them the conclusions that Yvain does? (ie, the population effect of smoking)

The MN scientist is lying. No, I didn't click on the link. Yes, I mean lying, not mistaken.

Comment author: RichardKennaway 12 August 2009 08:59:01AM *  0 points [-]

You can do better than correlations, but do you deny that you can draw from them the conclusions that Yvain does? (ie, the population effect of smoking)

The conclusion he draws is:

Even if we didn't know there was causation, it would at least help us to pick out who needs more frequent lung cancer screening tests.

Sure, standard statistics. No problem, for want of anything better.

On the other hand, if you want to know how the link between smoking and lung cancer works, the epidemiology can do no more than suggest places to look.

The MN scientist is lying. No, I didn't click on the link. Yes, I mean lying, not mistaken.

On closer reading, the actual scientific claim is less than I thought. It's a statistical study correlating the presence of a nitrosamine compound in the urine with lung cancer, and finding a higher correlation than with self-reported smoking. Original paper (full text requires subscription) here and blogged here. So just more statistical epidemiology and not at all epoch-making.

ETA: Extra links, just because these things are worth knowing.

Powered by Reddit Powered by Reddit