Weighting

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Weighting

Weighting (/ˈweɪtɪŋ/) is a statistical technique used in survey analysis that adjusts the results of a study to bring them more in line with what is known about a population. The term is derived from the Old English weġtung, meaning "the act of weighing".

Definition

In the context of statistics and data analysis, weighting is the process of assigning a weight, or degree of importance, to data points. This is often done to correct for bias or to give certain data points more influence on the outcome of an analysis.

Types of Weighting

There are several types of weighting methods used in statistics, including:

  • Inverse probability weighting: This method assigns weights based on the inverse of the probability of a data point being selected for the sample. It is often used in survey sampling to correct for non-response bias.
  • Propensity score weighting: This method assigns weights based on the propensity, or likelihood, of a data point being in the treatment group. It is often used in observational studies to balance the distribution of covariates between the treatment and control groups.
  • Importance weighting: This method assigns weights based on the importance of a data point to the research question. It is often used in machine learning to handle class imbalance problems.

Related Terms

  • Bias (statistics): Bias is a systematic error that can affect scientific investigations and distort the measurement process. Weighting is often used to correct for bias.
  • Sample (statistics): A sample is a subset of a population that is used to represent the entire group as a whole. Weighting can be used to ensure that the sample is representative of the population.
  • Survey methodology: Survey methodology is the study of survey methods, including survey design, sampling, question development, data collection, and data analysis. Weighting is a key component of survey methodology.

See Also

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External Links

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