# Non Probability Sampling

Please read the below paragraph and explain what is meant by: non probability sampling may not be as accurate as probability sampling methods

When the analysis is a based on an entire group or in other words all of the members of the group under study then the population is in place. A sample is taken when this population is too large to measure each one of the members. Think about it from this perspective, if you were investigating McDonalds restaurants, all the McDonalds restaurants in the world would be your population but the sample would be those restaurants that you study.

Probability sampling is similar to pulling a number out of a hat. In the McDonalds scenario if data is needed from the restaurants, one way for obtaining the information would be to draw a sample for a survey based on the zip codes of the specific restaurants. This would be a probability sampling. However, in non-probability sampling the researcher could simply decide to drive to a specific McDonalds restaurant and obtain the information through the survey. Although non probability sampling is a convenient way to conduct a survey, it is not as accurate or rigorous as probability sampling methods.

https://brainmass.com/business/business-math/non-probability-sampling-201106

#### Solution Preview

The statement that non probability sampling may not be as accurate as probability sampling methods is true because of several factors including the range the data cover and the size of the population.

For example, expanding upon the 'number in a hat' metaphor: let's imagine there were 1000 numbers in the hat ranging from a low of 40 to a high of 80 and with an average of 60. If you decide that it would take too long to draw all 1000 numbers and average them and you used non probability sampling and pulled one number from the hat (say a 53) you might draw the conclusion that the average number in the hat was at or near 53. You would be close to the truth, but not exact. What would happen if you drew a 40? You assumption that the average number was a 40 would be well off base and might lead you to faulty decisions that hinge upon the data. Of course, there is always the chance that you would draw a 60 and would make the right choice, but the conclusion ...

#### Solution Summary

The solution describes why non probability sampling isn't as accurate as probability sampling related to McDonalds.