Having access to the details of the potential research respondents can be challenging. There are a number of disadvantages of using simple random sampling in quantitative market research as follows: It also works as the basis for other probability sampling methods (Encyclopaedia Britannica, 2020).Īnother advantage is that this method is easy to assess sampling error. It is usually easy to use, particularly when the size of the target population is small. Simple random sampling is less complicated compared to many other sampling methods. As the researchers select potential respondents very randomly, each individual has the same chance of being selected. Unlike other sampling methods, simple random sampling is bias and prejudice free however, the condition is that it must be applied appropriately. There are a number of advantages of using simple random sampling in quantitative market research as follows: It is also sometimes simply called random sampling. It is where every member of the population has an equal probability (chance) of being selected. Simple random sampling involves selecting the sample at random from the sampling frame using either random number or a computer (Saunders et al., 2007). Like all other research tools, simple random sampling has its advantages and disadvantages. Sampling is a key part of any research, and many quantitative researchers use simple random sampling for the purpose of data collection. This article aims to identify and explain some of the advantages and disadvantages of simple random sampling. The Total Survey Error Approach: A Guide to the New Science of Survey Research.Advantages and disadvantages of simple random sampling Statistics in Plain English, Mahwah, NJ: Lawrence Erlbaum Rome, Italy: Food and Agriculture Organization of the United Nations Garbage in definitely equals garbage out. Statistics can only work with the data provided and, if your design is poorly thought out, will not be able to cover up these errors. The mistakes made by pollsters relate directly to any type of experiment involving random sample groups. Random Sampling Error and Experimental Design Despite this, opinion polls must always be taken as a guide only, not an exact representation of how an election is likely to unfold. ![]() Modern polling companies are very skilled at designing polls to select samples from many elements of the population, and via various media, so big errors rarely happen. The margins of error would be perfectly acceptable, in these cases, but the overall findings would still be horribly wrong. In addition, poorer families do not always have a fixed line telephone and use unregistered cell phones, again leaving a huge potential for inaccuracy. For example, an opinion poll company conducting telephone polls may make the mistake of only telephoning during office hours, when most of the population is at work, skewing the data. There have been many extremely inaccurate polls conducted over the years, and they fell down due to poor design and not understanding all of the relevant factors. In an opinion poll, there is no guarantee that the sample of 1000 or 10 000 people is truly representative of the larger population as a whole. However, this is a very narrow definition and is often misunderstood. They show the chances of the results in that group occurring purely by chance, exactly like the 95% confidence margin employed by many scientific researchers. The problem is that these results only show the random sampling error within that specific group. These are one of the most commonly misinterpreted representations of data, and failure to take into account the nuances of statistics can paint an incorrect picture. To illustrate how to ensure that your statistics are as accurate as possible, we are going to use the example of an opinion poll. ![]() Of course, when you use a sample group, it can never fully match the entire population, and there will always be some likelihood of random sampling error.Īny researcher must strive to ensure that the sample is as representative as possible, and statistical tests have inbuilt checks and balances to take this into account. ![]() In any experiment where it is impossible to sample an entire population, usually due to practicality and expense, a representative sample must be used. To further compound the random sampling errors, many survey companies, newspapers and pundits are well aware of this, and deliberately manipulate polls to give favorable results. ![]() Anyone who reads polls on the internet, or in newspapers, should be aware that sampling errors could vastly influence the data and lead people to draw incorrect conclusions.
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