When dealing with missing values in a dataset for analysis, what is a primary consideration that guides the decision to impute values rather than simply delete rows?

The primary consideration that guides the decision to impute values rather than simply delete rows with missing data is the critical assessment of the potential for significant information loss and the introduction of statistical bias. Deleting rows, often referred to as list-wise deletion or complete case analysis, removes any observation that has at least one missing value. This action directly leads to a reduced sample size, which diminishes the statistical power of the analysis. Statistical power is the probability of correctly detecting a true effect or relationship if one exists, meaning a smaller sample makes it harder to identify effects that are genuinely present in the underlying population. For example, if a dataset of 1000 records loses 200 due to deletion of rows with missing values, the remaining 800 records might be insufficient to reliably detect subtle but important patterns. Furthermore, if the missingness is not Missing Completely At Random (MCAR), meaning the absence of data is unrelated to any observed or unobserved variables, then deleting rows introduces bias. Missing Completely At Random implies that the missing data points are a random subset of all data points. However, if data is Missing At Random (MAR), where missingness depends on observed data (e.g., older respondents are less likely to answer a specific survey question), or Missing Not At Random (MNAR), where missingness depends on the value of the missing data itself (e.g., individuals with very low incomes tend to omit their income), then deleting those rows creates a non-representative subset of the original data. This distorted subset leads to biased parameter estimates and inaccurate conclusions, as the remaining data no longer accurately reflects the characteristics or relationships present in the full population. Imputation, conversely, is the process of estimating and filling in these missing data points with substitute values, which aims to preserve the maximum amount of available data, maintain statistical power, and mitigate the introduction of bias, thereby leading to more robust and accurate analytical results.