Understanding Population Sampling


Sampling Understood

Sampling theory states that by utilizing a systematic approach to selecting elements of a specific population one can, within a reasonable margin of error, collect data that pertains to the population as a whole.vThrough the use of sampling we successfully bypass the need to examine the entire population in question. Within the sampling theory two forms of sampling have been identified as follows:

Random Sampling (Probability Sampling)

This form of sampling provides that every element in a population has at least some chance of being selected as part of the sample and that this probability can be accurately calculated using some mathematical formula (Sampling, n.d.).

Nonrandom Sampling (Non-probability Sampling)
This form of sampling stipulates that there will be elements within the population which will not be selected at all, in other words there is no way to accurately determine the probability that an element will be selected from the population for the sampling (Sampling, n.d.). The majority of the time this is due to the fact that the sampling is being done based on assumptions or bias. By introducing a personal bias or by making assumptions as to what should be in the sample population, one has removed the ability to get results which are common across the entire population being sampled.


In Contest

Depending on your point of view there are benefits to either form of sampling, if there were not we would most likely not have 2 types. But when it comes to data and trying to find out what is really going on throughout a population, making the sample as random as possible is in my opinion the better path. This is due to the fact that spreading the research across the population in a random manner, utilizing a probability equation to decide on the sample population, you are more likely to get data which truly spans all types of elements which exist throughout the population.
In contrast when utilizing sampling which is not random you are immediately introducing assumptions and biases into the data which affects, in my opinion, the validity of the data itself as well as the statistical outcomes calculated from that data. I raise this as an issue because this question of validity is a real source of pain to everyone utilizing the statistics gatheredfrom that data to identify truths within the population.
Case-in-point, every automobile manufacturer which sells trucksin this country, states in their commercials that they are the consumer’s number one pick. But how can this be, number one is number one and there can only be one number one. This is because they do not use random samples, they use bias samples which provides data that supports a conclusion they have already made. Instead of a true desire to understand the population they are simply trying to make what they believe the truth.

Published by

Tim Clark

Experienced Business Owner, Chief Information Officer, Vice President, Chief Software Architect, Application Architect, Project Manager, Software Developer, Senior Web Developer, Graphic Designer & 3D Modeler, University Instructor, University Program Chair, Academic Director. Specialties: Ruby, Ruby on Rails, JavaScript, JQuery, AJAX, Node.js, React.js, Angular.js, MySQL, PostgreSQL, MongoDB, SQL Server, Responsive Design, HTML5, XHTML, CSS3, C#, ASP.net, Project Management, System Design/Architecture, Web Design, Web Development, Adobe CS6 (Photoshop, Illustrator)

One thought on “Understanding Population Sampling

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