Random sampling helps the researchers avoid the error due to any bias that they might have when doing research and collecting data. However, this method doesn’t allow the researchers to use any prior collected data.
So, we can say that while random sampling decreases the efficiency of a research process, it also helps reduce the likelihood of errors and biased results in these processes.
Here are some of the pros and cons of random sampling in which you randomly choose the samples.
Pros of Random Sampling
Data Analysis With Small Chances of Error
With the help of random sampling, researchers can collect data through a process which has less chances of having an error. This process is allowed because the samples are collected within specific boundaries. Since the samples are completely random, they produce results that represent the entire population. This way, you can easily reach good conclusions on many sampling and data analysis projects.
Less Knowledge Required
In this method, the researcher doesn’t have to have much knowledge of the data they’re collecting for research proposes. They can reach the results of any project without knowing anything about the data being collected. This is another great feature of random sampling.
Cons of Random Sampling
Additional Knowledge Isn’t Utilized
While random sampling can prevent the bias, it can’t prevent the bias in the process. For example, a researcher can easily choose specific regions for sampling where he thinks specific results can be obtained. This can be used to support their bias.
Takes a Lot of Time
In this process, you’ll need to review and check everything in order to make sure that it is tested properly. As a researcher, you’ll need to work with every person or thing one on one in order to isolate them from the others.