Description: Missing data refers to values that are not recorded or are absent in a dataset. This phenomenon can occur for various reasons, such as errors in data collection, system failures, or simply because certain data were not available at the time of collection. The presence of missing data can significantly affect the analysis and interpretation of results, as it can introduce biases or distort conclusions. In the field of data science and statistics, it is crucial to identify and properly handle these missing data to ensure the validity of models and inferences. There are different types of missing data, such as completely missing data, where no information is available for a specific variable, and missing data that occur at random, which can be addressed with statistical techniques. Managing missing data includes methods such as imputation, where missing values are estimated based on other available data, and sensitivity analysis, which evaluates how results may change with different assumptions about the missing data. In summary, missing data is a critical aspect of data science and statistics, and proper handling is essential for obtaining accurate and reliable results.
