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Missing values are the observations which the researcher plan to collect but could not collect or lost due to some reason. Many statistical tools cannot be employed when the data set has one or more missing values. In data collection through asking questions 'Don't know' response may also creep the problem of missing values. Utmost care should be taken by the researcher to avoid the missing values in the data set. Most common methods to deal with the problem of missing value while conducting the analysis is either to leave the observation, if possible, or to replace the missing value by the arithmetic mean of other collected observation.
Outliers are the observation which are quite different to other observations in the data set. Although all the statistical techniques can be employed when data set has outliers, their interpretations my be misleading. the most common reason of outliers being present in the data set is the recording error. This error should be corrected while editing and cleaning the data. Consider an example of survey of 100 customers in a mall.
If few bulk customers purchasing very large amounts are among the 100 surveyed customers. In this survey having outliers (bulk customers) may not be posing any error as bulk customers are always there in the mall along with small customers. However, in a similar survey at a nearby grocerry shop on a day when there is strike in the mall may include some bulk customers which could be misleading. Thus outliers should not be ignored as they might have some relevant information or pose to a serious risk.
Before detecting the outliers, we need to define them first. Commonly, an observation with a value that is more than 3 standard deviations from the mean is considered as an outliers, the researcher (discussed later) can also be helpful in indentifying the outliers. After identifying an outlier, the researches has to decide what to do with it. The researcher may like to delete it or modify the value of it or retain it as it is. It depends on the knowledge about the cause of that outlier.