Parametric methods are statistical techniques that assume a specific form for the underlying distribution of the data. These methods often involve parameters, such as the mean and variance, which define the distribution. Common examples include linear regression and t-tests, where the analysis relies on these assumptions to make inferences about the population. In contrast to non-parametric methods, which do not assume a specific distribution, parametric methods can be more powerful when the assumptions hold true. They typically require fewer data points to achieve reliable results, making them useful in various fields, including economics and psychology.