Why is it important that scientists test only one independent variable in a controlled experiment?

If more than 1 product of the experiment is changed and produces a different outcome, you can not tell which product coused the change.





Say a door squeaks when you open it. To find out what causes the noise, you remove the knob and add grease to the hinges. The problem is fixed, but there is no way of knowing what caused the squeaking to cease.Why is it important that scientists test only one independent variable in a controlled experiment?
Or it could have went away on its own. Hence the important assumption of 'ceterus paribus.'

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Why is it important that scientists test only one independent variable in a controlled experiment?
If you were to utilize two or more independent variables, you would not be able to tell which variable is causing the outcome.





For example: Let's say your car won't start, and you suspect that its either the battery or the car is out of gas. If you change the battery AND add gas and then your car starts, then you still don't know what the problem was. However by testing them separately (changing the battery, then trying to start the car, then adding gas and trying again) you can find out what the problem is.





This is the same principle used in controlled experimentation
To prove/disprove an issue with just the one independent variable. If you had several variables in the experiment, you would not know which variable really caused the end result. The experiment would lose the ';controlled'; element as, the numerous variables would result in unaccounted for results.





Think about it. If you were going to roll a metal marble down a street and you wanted to test magnetism or friction or the slope of the street but you had these variables: a big magnet along the path of the marble; a strong sideways wind perpendicular to the path of the marble, differing street surfaces (sand, concrete, grass, etc.) -- how could you tell which variable made the marble go off course? There are too many variables to know without several experiments, one for each variable.
if you have only one independent variable, it is easy to correlate the independent variable with the output. It makes things very easy.





There are ways to test multiple independent variables at a time. But it requires advanced statistical principles to determine how one variable affects the output.





Basically, it's easier to analyze the results mathematically with only one independent variable.
If you have 2 independent variables in the same experiment, then it is a lot more difficult to discern which independent variable, if not both, influenced the final result.


Using just one independent variable at a time ensures a 100% correct answer as to why the result changed: the one variable.
Haha, we just did this.


You should only change one indendent variable at a time because if you change more than one at a time, then you won't know what caused the result of the experiment.
because if they tested two or more variables, they wouldn't know which variable or variables verified the hypothesis or not.
I think you mean one at a time. I used 3 independent variables in my lab today in school.