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Sample data can be used to inform or support economic decision-making within an organization. Sample data is a data set that is selected from the larger population and that is often a representation of the whole sample. In this case, there is often a clearly defined procedure that is used to collect data. When a good method is used to select the sample and its size is sufficient based on the population size, it then follows that the resultant data can be as nearly reliable as the data for that could be collected from the whole population (Minnitt, 2007). In return, an organization can base its economic decisions based on the findings of analyzing this data.

Sample data can have a set of errors and lead to poor decision-making. Although sample data can be highly reliable when sufficient measures are put in place to guarantee quality and highly reliable sample, there may be chances of bias or misrepresentation of the population, which can then provide wrong information upon which to base the decision (Minnitt, 2007). For instance, an organization may seek to identify the approval rating of a particular program from the employees. From its large number of employees, say 30,000, the organization could choose to randomly interview 1000 employees distributed equally across all its facilities and representing all the diverse groups. The results can be a good representation of the overall workforce. Unfortunately, the organization could have misleading data in the event that it chose to collect data from a single facility, from a single race, a single job group, or from one gender.

Excellent explanation of how sample data can be utilized to support or inform economic decision making within an organization. Sample data in my opinion certainly has its pros and cons. As you mentioned, when sufficient measures are put into place to guarantee highly reliable data, sample data can be very beneficial. Though this is true, when not executed properly, the bad is really bad. Examples of errors in sampling can be categorized as non-sampling errors and sampling errors. Non-sampling errors are caused by factors other than those related to the sample selection while sampling errors occur solely as a result of using a sample from a population. From the example you have provided, what type of error do you believe is more likely to happen and why, non-sampling errors or sampling errors? Good initial posting!

 

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