Missing Completely at Random (MCAR) is a data missingness mechanism where the probability of missing data on a variable is unrelated to any other measured or unmeasured variables in the dataset. This implies that the missing data are a random subset of the complete data, allowing for unbiased statistical analysis if the missingness is truly MCAR.

Missing Completely At Random (MCAR)

Missing Completely at Random (MCAR) is a type of missing data mechanism where the likelihood of data being missing is unrelated to the data itself, either observed or unobserved. This implies that any analysis performed on the observed data will not be biased due to the missingness, making it the most straightforward missing data mechanism to handle statistically.

Missing Completely at Random

data missingness mechanism

probability of missing data

Measured variables

unmeasured variables

Missing data refers to the absence of data points in a dataset, which can lead to biased analyses and inaccurate results if not handled properly. Techniques such as imputation, deletion, and model-based methods are used to address missing data, each with its own advantages and limitations depending on the nature and pattern of the missingness.

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