![]() ![]() ![]() When creating a simple random sample, a researcher seeks to build an unbiased sample. The rules to build a sample are clear, and you don’t have to worry about additional steps, such as classifying the population into mutually exclusive subgroups as in stratified random sampling. Two of the most notable advantages of a simple random sample are its ease of use and perceived sense of fairness. Since the S&P 500 includes other industries, such as financials, health care, and tech, stratified random sampling may be more appropriate than simple random sampling to study this index. and Kroger Co.) would be misrepresenting the broader set. ![]() If your goal is to get a good sense of the stocks that make the S&P 500, then a sample of only companies in the consumer staples sector (think Procter & Gamble Co. Unlike the Nasdaq, an index mostly made up of tech companies, the S&P 500 represents companies from a broad range of sectors. Stratified random sampling can be useful with a diverse population. To avoid skewing the sample, each one of the 1,000 items can only belong to one category. To ensure your sample reflects the various traits, you can create a stratified random sample of 100 items by first dividing the 1,000 items into four categories and then randomly choosing an equal amount of items from each of the categories. Unlike simple random sampling, stratified random sampling requires that you classify the population into non-overlapping categories before creating a sample.įor example, imagine that in a population of 1,000 items, there are four distinct groups that share their own similar traits. Then, you select randomly from each subgroup to make sure that every group in the whole population is represented.Ī stratified random sample is useful when you need to ensure that a sample truly represents all subgroups in a population. Another one is stratified random sampling, in which you divide the population into “strata” - smaller groups with shared traits. Simple random sampling is one of many different ways you can sample a population. A researcher is generally willing to accept this potential room for error in exchange for not having to survey an entire population. However, the actual percentage of HBS students loving tacos could be 22%. One might conclude that 20% of the total population of 900 HBS students like tacos. For example, a simple random sample of 100 HBS students may show you that 20% of them like tacos. While researchers use simple random sampling to create an unbiased sample, they are aware that there is still room for sampling error. Finally, generate 100 random numbers using “=RANDBETWEEN(1,2000).” Match each of the 100 numbers with the corresponding company, and you now have a simple random sample of 100 companies from the Russell 2000. Second, assign each company a number from one to 2,000. ![]() First, copy and paste all 2,000 company names into a spreadsheet. Here are the three steps to create a simple random sample of 100 companies using Excel. To get a better sense of this index, you can carefully analyze a smaller, unbiased subset of companies that are representative of the index as a whole. Computer software often facilitates the process, letting you save your work, make edits, and handle large amounts of data.įor example, the Russell 2000 is an index that captures the stock performance of 2,000 of the smaller publicly traded U.S. Simple random sampling allows you to study a large group without having to take a look at every single item or person within the group. Match the numbers with the items on your list, and now you have your simple random sample.Īlternatively, you can select numbers using a random number table in a statistics textbook, or create a random number generator on a computer - the “RANDBETWEEN” formula in Excel is an example of doing this. Next, write each number on a small piece of paper and drop them all into a “lottery bowl.” Establish a total number for your sample and draw one piece of paper from the bowl until you reach your target sample size. First, you can number all items in the population sequentially. When a population is small, it’s relatively easy to create a simple random sample. Analysts use simple random sampling to build an unbiased sample and make inferences about the larger group. A simple random sample is a subgroup from a much larger group in which every item has an equal probability of being selected. ![]()
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