Iterative proportional fitting, also known as raking, is a statistical method used to adjust survey data to match known population characteristics. This is done in order to ensure that the survey results accurately represent the entire population, rather than just the sample of individuals who were surveyed.

One example of iterative proportional fitting is adjusting for the under-representation of certain demographic groups in a survey. For example, if a survey is conducted among a sample of individuals and it is found that the sample has a disproportionately low number of individuals from a certain racial or ethnic group, the survey results can be adjusted using iterative proportional fitting to account for this under-representation. The method involves comparing the demographics of the sample to those of the known population, and making adjustments to the survey results in order to more accurately reflect the overall population.

Another example of iterative proportional fitting is adjusting for the over-representation of certain subgroups in a survey. For example, if a survey is conducted among a sample of individuals and it is found that the sample has a disproportionately high number of individuals who are highly educated, the survey results can be adjusted using iterative proportional fitting to account for this over-representation. In this case, the method involves comparing the education levels of the sample to those of the known population, and making adjustments to the survey results in order to more accurately reflect the overall population.

Overall, iterative proportional fitting is a useful tool for ensuring that survey results accurately reflect the characteristics of the entire population, rather than just the sample of individuals who were surveyed. By making adjustments based on known population characteristics, the results of a survey can be more accurately and reliably used to inform decision making and policy development.