The covariate predicts partially the outcome e.g., the age of your subject. This variable does not affect the input. Whatever the results, the age of your subject is unchanged.
The confounder called also the third variable is related both to the dependent variable and the independent variable, for example, if your outcome is mortality and your predictor is the consumption of alcohol you find an association but you can t be sure that the rate of mortality is not associated with other factors like smoking, having a unhealthy diet in this case these factors are cofounders.
A covariate is an independent variable of your study. For example, if you want to assess the association of cholera outbreak against eating raw vegetables, eat out in restaurants and drinking untreated water. These are covariates. But a confounder variable is the one which has not been collected during your data collection but could affect the finding of your study either in the positive or negative direction. For example, in the above study if you found that those who ate raw vegetables were more likely to have cholera illness but if you did not collect information on their travel history your finding could have been confounded with their contact with cholera patients during their travel. Eating raw vegetables might not be really the risk but rather their contact history.
The definitions of neither term are particularly straightforward, as both of them depend on context (e.g., the conventions in a particular field of study). See these discussions on the Analysis Factor website, for example.
- https://www.theanalysisfactor.com/confusing-statistical-terms-5-covariate/
- https://www.theanalysisfactor.com/what-is-a-confounding-variable/
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