What are the implications of missing data?

What are the implications of missing data?

Missing data present various problems. First, the absence of data reduces statistical power, which refers to the probability that the test will reject the null hypothesis when it is false. Second, the lost data can cause bias in the estimation of parameters. Third, it can reduce the representativeness of the samples.

Why do missing values occur?

In statistics, missing data, or missing values, occur when no data value is stored for the variable in an observation. Sometimes missing values are caused by the researcher—for example, when data collection is done improperly or mistakes are made in data entry.

What are the effects of missing data in statistics?

Missing data present various problems. First, the absence of data reduces statistical power, which refers to the probability that the test will reject the null hypothesis when it is false. Second, the lost data can cause bias in the estimation of parameters.

How is a missing value replaced in an imputation?

One of the most widely used imputation methods in such a case is the last observation carried forward (LOCF). This method replaces every missing value with the last observed value from the same subject. Whenever a value is missing, it is replaced with the last observed value.

Which is the maximum likelihood method for missing data?

Expectation-Maximization (EM) is a type of the maximum likelihood method that can be used to create a new data set, in which all missing values are imputed with values estimated by the maximum likelihood methods.

What happens if data is missing due to equipment failure?

However, if data are missing by design, because of an equipment failure or because the samples are lost in transit or technically unsatisfactory, such data are regarded as being MCAR. The statistical advantage of data that are MCAR is that the analysis remains unbiased.