Statistics and independent variables

AI Thread Summary
In statistical analysis, when an independent variable A is influenced by other variables B, C, and D, it is crucial to specify the conditions under which A causes an effect. The discussion highlights that A only causes "something" when B, C, and D are at specific values (B2, C2, D2), indicating that A's influence is conditional. It raises the question of whether A can be considered a causal factor in general or only under those specific conditions. The term "correlates" is suggested as a more appropriate descriptor when discussing relationships without asserting causation. Understanding these nuances is essential for accurate statistical interpretation.
Cinitiator
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Homework Statement


Let's say the independent variable (in statistical terms) A depends on variables B, C and D. We perform tests, and find out that the variable A causes "something" with the values of B, C and D equal to B2, C2 and D2.

Let's also say that A with variables B, C and D of B1, C1 and D1 won't cause "anything" (as in, won't cause "something").

How does one deal with such problems in statistics? Whenever some variable depends on other variables to a great extent (even if these variables aren't external), is it usually said that it has been concluded that variable A causes something ONLY with variables B, C and D equal to B2, C2 and D2?

Or is it said that variable A causes something, regardless of the variables that it depends on?


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The Attempt at a Solution


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Cinitiator said:
Let's say the independent variable (in statistical terms) A depends on variables B, C and D. We perform tests, and find out that the variable A causes "something" with the values of B, C and D equal to B2, C2 and D2.
So you discover that A is not an independent variable? It might better serve your query if you were to come up with a real world example to illustrate your question, and also indicate what year of maths you are studying at school.

Perhaps the word "correlates" might be useful here, as it doesn't ascribe a causal relationship. (Example: the height of a man correlates strongly with the length of the trousers he buys.)
 
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