Correlations and Confounding Variables

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Correlations & Confounding Variables Correlations are measurements on the various variables that show a relationship among the variables (Correlations, 2013). They determine an association between variables and how variables are associated with each other. Confounding variables are third party variables that can show relationships among the dependent and independent variables without presenting a viable relationship with the individual study (Spunt, 2011). The confounding variables can show relationships that are not necessarily true and do not prove changes in variables are caused by other variables. Correlations do not always mean that the changes in variables cause changes in other variables and the confounding variables can cause a correlation that is not necessarily true. Correlation measurements can show if and how variable pairs are related (Correlation, 2012). Quantifiable, or specific amounts, of data are used to determine statistical significance in measurements. The statistical significance determines how likely the correlations are viable measurements or can be due to sampling errors or confounding variables that may not show true results. Significance levels are used to determine the viability of the measurements. Confounding variables, such as weather, type, scope, etc. can be associated with independent and dependent variables, but not necessarily prove an association or changes between the variables in a study. They do not prove that a dependent variable
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