6. When you have a group of researchers pursuing information, then there is the possibility that a conscious or unconscious bias may apply to the application of this data. Because many groups can have an extensive size, having a full list of each person who could be randomly drawn may be impossible. The random Ness of this process creates, under most circumstances, a balanced subset which carries the most potential for representing the larger group as a whole. Simple random sampling occurs when a subset of a statistical population allows for each member of the demographic to have an equal opportunity of being chosen for surveys, polls, or research projects. Investopedia uses the example of a simple random sample as having the names of 25 employees being chosen out of a hat from a company of 250 workers. This process can also remove the classification errors that can occur in other forms of information collection. Researchers must include every person or circumstance selected through the random sampling process to complete the work. 3. Simple random sampling reduces this risk by allowing for multiple types of randomness in the selection of the individuals or circumstances being studied. Although there are distinct advantages to using a simple random sample in research, it has inherent drawbacks. The probability of incurring errors in sampling increases with decreased sample size. If the sample is not large enough to represent the views of the entire population during the first round of simple random sampling, purchasing additional lists or databases to avoid a sampling error can be prohibitive. Simple random sampling uses common recording skills and standard observation techniques to collect information. Simple random sampling offers researchers an opportunity to perform data analysis and a way that creates a lower margin of error within the information collected. 7. It offers an equal chance of selection for everyone within the population group. 5. The usefulness of simple random sampling with small populations is actually a disadvantage with big populations. It relies on the quality of the researchers performing the work. Although the margin of error is typically lower with this process, you could also end up with inaccurate results because the random pulling managed to include more weight on one side of the equation than the other. There must be a larger size available to use this method. As its name implies, producing a simple random sample is much less complicated than other methods, such as stratified random sampling. Simple random sampling is effective because of how its structure can limit the influence of an unconscious bias. Random sampling is very convenient when working with small populations that have already been identified and listed. There can be some disadvantages because of the overall simplicity of this process, but it typically allows for a greater understanding on specific questions or needs without the costly processes of qualification that other research methods may mandate. This creates, in most cases, a balanced subset that carries the greatest potential for representing the larger group as a whole. To ensure bias does not occur, researchers must acquire responses from an adequate number of respondents, which may not be possible due to time or budget constraints. Copyright 2020 Leaf Group Ltd. / Leaf Group Media, All Rights Reserved. If done right, simple random sampling results in a sample highly representative of the population of interest. When a sample set of the larger population is not inclusive enough, representation of the full population is skewed and requires additional sampling techniques. Advantages of sampling. That is why the overall size of a survey is limited in its scope. For example, suppose you want to know how your candy bar slogans work with tennis-playing single men ages 18 to 45. Nick Robinson is a writer, instructor and graduate student. What makes cluster sampling such a beneficial method is the fact that it includes all the benefits of randomized sampling and stratified sampling in its processes. A simple random sample is meant to be an unbiased representation of a group. Obviously, you can't test the slogans on everyone in the world, so you need to select a sample population for testing. Given here are the advantages of Simple random sampling. Australian Bureau of Statistics: Sampling Methods, University of California, Los Angeles: Simple Random Sampling, University of Hawaii: Sampling Strategies and Their Advantages and Disadvantages. h��X�n�8�>6(R�2�E�Ĺ�m� j�fAJ�&ːU���ΐ��Nl�.�5"g����3΄����I!��LZ*��b �*��fN�=ֹ�xIM�� pԅ�.��3��� Thanks to the randomization of selection, the entire population receives usable observations that can offer specific insights at the individual level even though a small group (and not the entire population) was surveyed or studied. endstream endobj 80 0 obj <> endobj 81 0 obj <> endobj 82 0 obj <>stream Have you ever watched a roulette wheel at a casino? Researchers using this method may not need to hold industry-specific experience to produce results, but they do need to have information collection experience to be effective at what they do each day. The drawbacks of this research method include: In simple random sampling, an accurate statistical measure of a large population can only be obtained when a full list of the entire population to be studied is available. Samples are used in statistical testing when population sizes are too large.