A researcher found a strong, positive correlation that is statistically signific

Question

A researcher found a strong, positive correlation that is statistically significant between the number of books in a child’s home and the reading level of 10-year-old children. Given this evidence alone, why can’t the researcher conclude that having books available in the home causes improved reading skills?

How does an experiment differ from a correlation study?

What is the difference between an independent and a dependent variable? Provide some examples.

What is a confounding variable?

How are confounding variables problematic? Provide some examples of confounding variables in an experimental design.

How do double-blind designs and random assignment help us reduce the effects of confounding variables?

Explanation / Answer

1. the reseacher who found a strong, positive correlation that is statiscally significant between the number of books in a child's home and the reading level of a 10 year old child cannot conclude that having books at home causes improved reading skills based on this evidence alone as the size of the sample of reseach is not enough. by taking only one child into account against an enormous population for the reseach is not proportionate . Hence nothing can be concluded by this.

2. In a correlation study we study existing datas. it determines whether there is relationship between two or more co-variables. a correlation is used to see if the existing variables are predictive of each other. whereas an experiment establishes a cause and effect relationship. it manipulates the independent variable to observe its effect on the dependent variable. in an experiment outside factors are also controlled.

3.the outside influence that changes the effect of an independent and dependent variable in an experiment is known as the confounding variable. these variables are the ones that the reseacher failed to control and eliminate. confounding variables often ruin the desired outcome of an experiment

4.confounding variables often ruin the experiments and produce useless results. examples- in an experiment of the speed of a ball and its weight the most likely result would be that when the ball is thrown at most force it will reach faster however in a situation like this a confounding variable would be the flow of the wind which can either increase or decrease the speed of the ball. this confounding variable would produce false results if they are not controlled by the reseacher.

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