Open thread, 23-29 June 2014

David_Gerard

Previous open thread

If it's worth saying, but not worth its own post (even in Discussion), then it goes here.

Notes for future OT posters:

1. Please add the 'open_thread' tag.

2. Check if there is an active Open Thread before posting a new one.

3. Open Threads should be posted in Discussion, and not Main.

4. Open Threads should start on Monday, and end on Sunday.

Previous open thread

If it's worth saying, but not worth its own post (even in Discussion), then it goes here.

Notes for future OT posters:

1. Please add the 'open_thread' tag.

2. Check if there is an active Open Thread before posting a new one.

3. Open Threads should be posted in Discussion, and not Main.

4. Open Threads should start on Monday, and end on Sunday.

After all, you can more or less divvy up the possibilities as:

A causes B B causes A both A and B are caused by C

There are at least two more possibilities: A and B are unrelated, but happen to be in sync for a while, and the data was collected wrong in some way.

You could say that "A and B happen to be in sync for a while" is possibility 3, where C is the passage of time. (Unless by "happen to be in sync for a while" you mean that they appear to be correlated because of a fluke.)

0[anonymous]12y

To generalize, it's also possible that you're observing survivor effects, i.e., both A and not B (or B and not A) cause the data to appear in your data set.

4gwern12y

I'm choosing to ignore that possibility to clarify the exposition of what I think is going on. Problems like that are what I'm referring to when I preface: [...] Even if we had enormous clean datasets showing correlations to whatever level of statistical-significance you please, you still can't spin the straw of correlation into the gold of causation, and the question remains why.

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