However, there are limitations with random samples. It requires individuals within the population to volunteer and provides information. The people who usually participate may have the strongest opinion on the topic being analyzed. This brings the question of accuracy of the representation of such population (Little, 2016). There are two main limitations of random sampling. These are biased outcomes and laborious and time-consuming. Biased results are brought by the fact that the population of interest produces the people to be a sample. If the question is about things affecting them, they may be biased in providing information (Little, 2016). Random sampling is also time-consuming and involves a lot of things to be done hence laborious.
To have a truly random sample, one will need to have access to the target population and be able to assign each participant randomly to a condition. This is ideal since only random assignment to people by researchers before manipulation can provide causal interpretability of results (Little, 2016). To prevent problems and limitations with random sampling, there is need to ensure that you have a group which is bigger and each member has different views.
Comment 2 ( this is an answer for the teacher about my answer) concerning this question. Explain each sampling technique discussed in the “Visual Learner: Statistics” in your own words, and give examples of when each technique would be appropriate.
Thanks you Rosy for your answer. When doing statistical interpretation of any data, we always hope to achieve a good understanding of the estimated population parameters. Naturally, we do this using observed data from various samples.
But how do you realistically determine the sample size based on confidence level?
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