If you are doing experiment on a large group of sample which method of controlling will you adopt?

The correct answer is: C. Elimination and matching

Elimination and matching are both methods of controlling for confounding variables in an experiment. Confounding variables are variables that are associated with both the independent variable and the dependent variable, and can therefore make it difficult to determine whether the independent variable is actually causing the change in the dependent variable.

Elimination involves removing participants from the study who have confounding variables. This can be done by excluding participants who have certain characteristics, such as age, gender, or health status.

Matching involves finding participants who are similar in terms of their confounding variables, and then randomly assigning them to either the experimental group or the control group. This helps to ensure that the only difference between the two groups is the independent variable.

Both elimination and matching are effective methods of controlling for confounding variables. However, matching is often more difficult to do, as it requires finding participants who are similar in terms of many different variables.

In the case of a large group of sample, it is often not possible to eliminate all of the confounding variables. In this case, matching can be used to control for some of the most important confounding variables.

For example, let’s say you are conducting an experiment to test the effectiveness of a new drug for treating high blood pressure. You could eliminate participants who have other health conditions that could affect their blood pressure, such as diabetes or heart disease. However, it would be difficult to eliminate all of the possible confounding variables, such as age, gender, and diet. In this case, you could match participants on these variables before randomly assigning them to the experimental group or the control group.

By using both elimination and matching, you can help to ensure that your results are accurate and that the independent variable is actually causing the change in the dependent variable.