In order for researchers to be able to say that variable a causes variable b, they must eliminate the possible influence of

a. correlational variables
b. independent variables
c. dependent variables
d. confounding variables

Answers

In order for researchers to be able to say that variable a causes variable b, they must eliminate the possible influence of "confounding variables."


A confounding variable is an outside impact that progressions the impact of a reliant and autonomous variable. This incidental impact is utilized to impact the result of an exploratory plan. Basically, a confounding variable is an additional variable went into the condition that was not represented. Confounding variables can destroy a test and create futile outcomes. They recommend that there are relationships when there truly are most certainly not. In an examination, the autonomous variable by and large affects the dependent variable.  

D.

Explanation:

Confounding variables can be termed as the variables not counted in for account. They are the 'extra' variables. Such variables can hamper the experiments and produce a useless result also.

Such variables causes the introduction of bias in an experiment and can increase the possibility of variances. And to avoid or to eliminate such possibilities, confounding variables should be avoided in research.

So, the correct answer is option D.

Confounding variable

Explanation:

The confounding variable is the variable that is extra in variables. It is not counted by the experimenter in the research experiment. With the help of confounding variables, it can be explained that it is cor-relational but in actual it is not there. This variable introduces the bias ness in the experiment.

confounding variable is the variable that affects the variable hidden on its outcome. But technically if we see confounding is not a true bias. Bias occurs when there is an occurrence of an error in data collection. In confounding variable there is positive bias occur when an association of bias is away from null and negative bias occur when it is associated with null.  



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