A confounding variable is an extraneous variable that affects both the independent variable and dependent variable, making it difficult to determine their true relationship. It can lead to misleading conclusions about cause-and-effect relationships.
Imagine you're trying to study whether eating ice cream causes sunburns. You collect data on ice cream consumption and sunburns but fail to consider exposure to sunlight, which is actually causing both variables independently. In this case, sunlight acts as a confounding variable because it influences both ice cream consumption and sunburns.
Independent Variable: An independent variable is manipulated by researchers in an experiment to observe its effect on another variable (the dependent variable). It should be carefully controlled to avoid confounding.
Dependent Variable: A dependent variable is the variable that is being measured or observed in an experiment. It is expected to change as a result of changes in the independent variable.
Control Variable: A control variable is a factor that remains constant throughout an experiment to ensure that any observed effects are due to the independent variable and not other variables. It helps minimize confounding.
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