Why is a sample used more often than a population?

Question

Accepted Answer

Hello there. Today we want to solve the following practice problem together. So first off, let us read the problem and highlight all the key pieces of information that we need to use in order to solve this problem. Why is it more practical to use a sample instead of an entire population when conducting a study? Awesome. So it appears this part for this particular problem, we're asked to determine why it's considered more practical to use a sample instead of an entire population when conducting a study. So now that we know we're ultimately trying to solve for, let's take a moment to read off our multiple choice answers to see what our final answer might be. So A is sampling reduces cost and time while still providing representative data. B is sampling eliminates errors entirely from data collection. C is sampling guarantees 100% accuracy in all situations. And finally, D is sampling is preferred because populations are always inaccurate. OK. So first off, in order to solve this particular problem, let us define the difference between a sample and a population. So, as we should recall for a sample, which I'll just call S for short, So for a sample, as we should recall, it is a subset of individuals selected from a larger group, a population, for the purpose of analysis, whereas, whereas a population, which I'll call P is, as we should recall, a population on the other hand, includes all members or items of interest for a particular study. So now, our next step is we need to recall reasons that we should use or reasons for using a sample. So as we should recall, so I'll call it reasons, so R for short, so reasons for using a sample. Firstly, We have cost efficiency, which I'll call CE for short. We have cost efficiency because that's a good reason for using a sample because collecting data from an entire population can be expensive and time consuming, whereas a sample provides a more manageable and cost-effective alternative. There's also time constraints. So if you have a time constraint, studying an entire population will often take much longer than analyzing a sample, because the sample is only a part of the population, not the entire population. And that would mean that using a sample would be more practical and time efficient. And Also, it's more feasible. Which I'll just use the letter F to denote feasible. And for this particular case, as we should recall and note, in many cases it is physically impossible to gather data from every individual within a large population. That's why using a sample, which once again is a piece of the population would be more feasible and more likely to do in a in a certain period of time, especially if there's a time constraint. So with that in mind, our last reason for using a sample is accuracy and precision. When carefully selected, a sample can accurately represent a population as a whole and provides reliable results without examining every individual separately. So that means that in conclusion, Without a doubt, using a sample is often necessary because it allows for quicker, cheaper, and more feasible data collection while maintaining the accuracy. That is needed to to collect a good data set when the sample that is being studied is considered representative. So that means without a doubt, our final answer has to be multiple choice answer letter A, which once again is sampling reduces cost and time while still providing representative data. And that's it, we've officially solved for this problem. Thank you so much for watching. Hopefully that helped, and I can't wait to see you in the next video. Bye.

Why is a sample used more often than a population?

Key Concepts

Sampling

Cost and Time Efficiency

Statistical Inference

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