Suppose that a study of children who were 9 to 14-years-old found that the larger a shoe size that a child has, the higher the s

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3 years ago

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Suppose that a study of children who were 9 to 14-years-old found that the larger a shoe size that a child has, the higher the s

core they got on a mathematical skills test. One interpretation might be that increased shoe size causes improved quantification skills. What might be a confounding variable in this relationship? Please give your answer and explain in no more than three sentences.

Social Studies

2 answers:

lorasvet [3.4K] · Answer 1 · 2021-12-04T07:53:35+03:00

Answer:Some individuals may be natural good at maths or may be just smart kids who are good with mathematics

Explanation:Some students may be intelligent or good at maths which means the shoe size will not be the cause of their quantification skills.

A confounding variable refers to another variable that you may have not take notice of but which is very likely to act on the variable you are trying to measure and make that change you are looking for , its an extra variable. It can mess up your experiment and give you result that are not caused by what your projected. They can assume a correlation that doesn't exist and insert bias in your study. For this reason you have to know these variable before you start your research or study in order to avoid them.

liberstina [14] · Answer 2 · 2021-12-04T09:49:13+03:00

The correct answer is the skills of the children in math.

In statistics, a confounding variable also known as a confounding factor, a confusing variable or confounding factor is a strange variable that is positively or negatively associated with both the explanatory variable and the response variable. We need to control these factors to avoid incorrect beliefs that the response variable is associated with the explanatory variable.

Confusion is a major threat to the validity of inferences made about statistical associations. In the case of a confounding variable, the association observed with the response variable should be attributed to the confounding factor, instead of the explanatory variable. In science, we test for confounding factors including these third, or fourth, or fifth, or sixth variables, in statistical models to explain the association of interest.