Explain what should be done with suspected or missing data.

Handling suspected or missing data involves several steps:

1. Identify Missing or Suspected Data: The first step is to identify the missing or suspected data. This can be done by using various data analysis tools or software. You can also visually inspect the data to identify any gaps or inconsistencies.

2. Analyze the Nature of the Missing Data: Once you have identified the missing data, the next step is to analyze the nature of the missing data. Is the data missing completely at random? Is it missing at random? Or is it missing not at random? Understanding the nature of the missing data can help you decide on the best method to handle it.

3. Decide on a Method to Handle the Missing Data: There are several methods to handle missing data. You can choose to ignore the missing data, especially if it is missing completely at random and it constitutes a very small percentage of the total data. However, this is not usually recommended as it can lead to biased results. Other methods include imputation, where the missing values are filled in based on other available information, and deletion, where the rows with missing data are deleted.

4. Implement the Chosen Method: Once you have decided on a method, the next step is to implement it. This can be done using various data analysis tools or software.

5. Check the Results: After implementing the chosen method, you should check the results to ensure that the missing data has been properly handled. This can be done by visually inspecting the data or using statistical tests.

6. Document the Process: Finally, it is important to document the entire process. This includes the methods used to identify the missing data, the analysis of the nature of the missing data, the chosen method to handle the missing data, the implementation of the method, and the results. This documentation can be useful for future reference and for other researchers who may use the data.