Which statement is true regarding missing data?

The statement that the type of statistical analyses chosen must match the type of missing data present is correct because different types of missing data (such as missing completely at random, missing at random, and missing not at random) require specific handling techniques to mitigate bias and maintain the integrity of research findings.

When data is missing, researchers must consider not only how much data is missing but also the mechanism behind the missingness. For example, if data is missing completely at random, certain types of statistical analyses may be more robust and less affected by the missing values. Conversely, if data is missing not at random, more complex methods that account for the reasons behind the missingness are necessary to avoid skewed results.

Using the appropriate statistical methods tailored to the type of missing data ensures that analyses yield valid conclusions and helps minimize the potential impact of missing data on research outcomes. Each type of missing data requires a different approach to ensure that results are reliable and informative.