When it comes to experiments, one needs to take confounding variables into consideration. When an experiment is conducted, the major aim and focal point remains to showcase the cause and effect relationship amidst the dependent and independent variables. However, a confounding variable comes into play when there are two or more independent variables present whose outcomes are difficult to decipher. Thus, the results of the experiment lead to confusion as the effect of one independent variable on the dependent one is indistinguishable from the alternate independent variable. Hence, a confounding variable may be classified as an ‘extra’ that was not accounted for initially. These variables can not only ruin an entire experiment but they can significantly toy with the authenticity of the results. In fact, many confounding variables indicate to a correlation between factors when there is actually none present. Due to this chaos, many biases are introduced within an experiment. Hence, it is extremely crucial to fully the grasp the concept of a confounding variable in order to conduct a successful experiment.
In order to fully comprehend the idea of a confounding variable, a hypothetical situation may be considered. A fine example of this is the interdependence between murder rate the increasing sale of ice cream. In this situation, many can suggest that these murders are causing people to buy the ice cream. However, since this is highly improbable, this scenario can be judged from another point of view. It can also be suggested that buying ice cream leads individuals to commit murder. This also proves to be highly unlikely. In addition to this there exists a third variable which may be identified as the confounding variable. It is remotely possible that the weather is causing a correlation. When the weather is extremely chilly, there is a decrease in the number of individuals who are interacting with others and thus they are less likely to buy ice-cream. On the contrary, when the weather is hot outside, there is an increase in social interaction and thus more ice cream is being bought. Thus, in this situation the weather is the confounding variable which blurs the relationship between the rate of ice cream sales and the consequent murders.
There are numerous amounts of ways that can be embraced in order to annihilate the presence of confounding variables in an experiment. Firstly, it is essential to identify all of the viable confounding variables that can possibly exist. Secondly, one must analyze whether these factors would actually affect the outcome of the study. In order to figure this out, it is important to research and discover any academic databases in order to garner ideas of all the possible confounding variables. Moreover, any kind of potential biases can be abolished with random samples. Finally, counterbalancing can be incorporated in the experiment to avoid confounding variables. For example, half of the group may be measured under a particular condition while the other half is analyzed under a separate condition. Thus, confounding variables can be easily eliminated if certain precautions are taken before hand.