Data can be missing for various reasons. This poses some challenges when doing d

Question

Data can be missing for various reasons. This poses some challenges when doing data analysis. For example, suppose you wanted to do some analysis of the yearly incomes of the employees at Wayne Corporation. When asked for their incomes, 25% of the employees did not participate in the survey; therefore, their incomes are missing from the dataset. How would you summarize the income data in this case? Is it appropriate to ignore the missing incomes and summarize the data without them? Should you estimate the missing incomes, perhaps with the overall average, to complete the data set?

Explanation / Answer

If 25% of data are missing, so, we may summarize the income data by using 75% of data. We know that sample mean is a good estimator of population mean. So, mean income of 75% data is an estimator of the mean income of the whole data and in such case we may ignore the 25% of data. If we consider average income of whole data, then we may estimate average value of 25% data by using an appropriate statistocal tool to complete the dataset.

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