“If you don’t believe in sampling theory, next time you go to the doctor and he wants to take a little blood, tell him to take it all.”
– Gian Fulgoni
Hi, this is Jerry Garrett, and I’m back with a post for this week that should be of interest to you. The topic of today’s lesson will be sampling, and we will be diving straight into it. As a result of this post, you will be able to fully understand what sampling is and how it works by the time you reach the end of it. So let’s take a look at what we’ll be doing as soon as possible!
It is impossible to fully analyze a system that is too large and complex to be analyzed in its entirety. Can you predict potential problems without spending millions of dollars or examining every single detail of the situation? As a result, sampling becomes a crucial component in the decision-making process. To perform the analysis, a small, random slice of your total output is taken and is used to generate data from which the entire system can be analyzed.
Let’s take a look at an example of a blood test that would be performed in a hospital. In order to get accurate results, only a small amount of blood is taken for testing. It is not necessary to take all of your blood for analysis. This is beneficial to the process as it makes it more efficient and manageable.
As a result of sampling, you are able to identify errors without having to test every part of the system, thus saving you both time and money. Imagine that you are manufacturing mobile phones: instead of examining every single one of them, you would test only a few to see if there are any issues and fix them before they become widespread.
A second method of sampling is the spot check – the systematic selection of an object at random for examination, just like the police do during traffic stops to check whether it is a crime. As soon as they come across an issue, they address it immediately in order to resolve it.
This concludes the post for this week. Thanks for reading! I look forward to seeing you next week!