A confounding variable (also confounding factor, lurking variable, a confound, or<unk>  is an extraneous variable in a statistical model that correlates (positively or negatively) with both the dependent variable and the independent variable. The methodologies of scientific studies therefore need to  control for these factors to avoid what is known as a type 1 error: A 'false positive' conclusion that the dependent variables are in a causal relationship with the independent variable. Such a relation between two observed variables is termed a spurious relationship. Thus, confounding is a major threat to the validity of inferences made about cause and effect, i.e. internal validity, as the observed effects should be attributed to the<unk>  rather than the independent variable.
For example, assume that a child's weight and a country's gross domestic product (GDP) rise with time. A person carrying out an experiment could measure weight and GDP, and conclude that a higher GDP causes children to gain weight, or that children's weight gain boosts the GDP. However, the confounding variable, time, was not accounted for, and is the real cause of both rises.
By definition, a confounding variable is associated with both the probable cause and the outcome. The<unk>  is not allowed to lie in the causal pathway between the cause and the outcome: If A is thought to be the cause of disease C, the confounding variable B may not be solely caused by behaviour A; and behaviour B shall not always lead to behaviour C. An example: Being female does not always lead to smoking tobacco, and smoking tobacco does not always lead to cancer. Therefore, in any study that tries to elucidate the relation between being female and cancer should take smoking into account as a possible<unk> . In addition, a<unk>  is always a risk factor that has a different prevalence in two risk groups (e.g. females/males). (Hennekens, Buring & Mayrent, 1987).
Though criteria for causality in statistical studies have been researched intensely, Pearl has shown that confounding variables cannot be defined in terms of statistical notions alone; some causal assumptions are necessary. In a 1965 paper, Austin Bradford Hill proposed a set of causal criteria..
Many working epidemiologists take these as a good place to start when considering confounding and causation.  However, these are of heuristic value at best.  When causal assumptions are articulated in the form of causal graph, a simple criterion is available, called backdoor, to identify sets of confounding variables.
Confounder
Limiting factor
Confounding
Impact factor
Random variable
Mass effect 
Cognitive bias
Degrees of freedom 