2. L URKING V ARIABLES & T HEIR C ONSEQUENCES

3. L URKING V ARIABLES Lurking Variable Variable not in your study that can (and probably does) effect the interpretation of the relationship between your two measured variables Often makes up the “left over” r 2 May be hidden Can cause a “strong” or “weak” relationship that isn’t true Dangerous to data and Interpretations

4. W HAT D O W E D O A BOUT LV’ S ???? Identify as many as possible before study Attempt to Control them through experimental design Identify their potential effects Discuss about their impact in your conclusions

5. C AUSATION r and r 2, our regression statistics are describing an association between 2 variables. But does this association mean that the explanatory variable CAUSES the response variables This is where a LURKING variable comes into play… Does the type of food effect fish growth

6. C AUSATION ( VISUALLY ) Below are three different visual examples of different situations and underlying variables that can Explain an association xy Dotted lines = association Arrow = causal relationship Causation xyz Confoundin g Causation doesn’t mean there aren’t other factors that effect the result… Just that the response is directly caused by the explanatory variable… When you’re not sure if the lurking variable is causing the response or if it’s the variable you’re wanting to study

7. C AUSATION ( DIRECT ) Let’s look at situations where direct causation occurs A study of recorded the heights of young males (between the ages of 12 and 15) and their fathers. The study found an association between the two heights with an r 2 of about 25%. While there is a direct cause between the thickness of the rat’s stomach and the ounces of battery acid eaten, this is an example of a situation that you can’t generalize to all cases. IE… The effect might not be the same for humans. There is a direct causal relationship between the height of a father and their son through heredity. It is possible to have direct causation with a low r 2, it just says that the father’s height only explains about 25% of the variation in the son’s height. A study performed on a number of lab rats found an association between the number of ounces of battery acid eaten and the thickness level of the stomach lining.

8. C ONFOUNDING Two variables are “confounding” when you can’t tell which variable is effecting the response In this course, you may study the effect of the different types of fish food on their growth rate. While you are looking for a causal relationship between these two variables, what are some lurking variables that may change your results and effect the growth rates? While the type of food does effect growth rate, there are other factors that can cause that growth rate change like: 1)Dead fish or debris in the tank INCREASES the amount of ammonia in the tank (causing growth rate to decrease) 2)Can you think of more? The MORAL: Association doesn’t mean CAUSATION Ammonia

9. S O W HEN C AN I SAY CAUSE? Remember, even HIGH correlation doesn’t mean CAUSATION When can I say it? If you do an EXPERIMENT and control lurking variables OR if you can prove high association over repeated studies, then you can say the magic word!!!

10. M ORAL O F THE S TORY Correlation and Association doesn’t mean CAUSATION Really examine the CONTEXT of your data Don’t just look at the numbers Numbers tell you everything!! I love Numbers!! Don’t listen to that Geek! You better look at the CONTEXT & the Experiment, not just the numbers.