Question

How can a researcher avoid a biased sample?

Answer 1

We can answer this question by listing out the various methods in which a researcher can avoid a biased sample. We state these methods such as simple random sampling, stratified random sampling, adjusting the sample size and variation, etc. We also give a brief description of what each of them are and give an example or two.

Complete step by step solution:
Biased sample is a statistical term used for samples which are obtained such that some of the samples are given a higher priority or higher probability compared to other samples. This arises if the researcher does not select his population of interest appropriately, thereby leading to wrong or undesirable outcomes.
In order to avoid bias sampling, we can use various methods as listed below:
Simple random sampling: This method required researchers to select samples randomly that are purely based on chance. Therefore, by doing this it ensures that every segment of the population is reached out to and has a person to participate in the research. An example similar to this is the randomize function in the scientific calculator. It is used to generate a random number in the given range.
Stratified random sampling: This method has the researcher doing some study on the population that he wants to gather data from in order to represent the samples precisely. This can be done by properly segmenting the group of persons and determining the samples appropriately.
Adjusting the sample size and variation: The researcher has to adjust the sample size in such a way as to contain a sufficiently large sample space of the population. He should also have a wide variety in his samples not targeting a specific set of persons but is more wide spread.
Hence, by following such methods, a researcher can avoid a biased sample.

Note: It is essential to know the concepts of biased sampling in statistics. It is of great interest for the researchers to know more about this so that they can carry on their research with minimum biasing. They need this information to make sure they do not end up making any mistake while selecting the part of the population for his or her research.