Population has an equal chance of being chosen, a random sample is the best at removing possible bias.Įxample 1: Identifying Why a Given Statement Does Not Describe a Simple Random Sample One way of constructing a simple random sample wouldīe to label the students from 1–200 and then use a random number generator to choose 50 numbers. We call a sample where all of the members have anĮqual chance of being chosen a simple random sample or even just a random sample. We can improve on this sample method by instead choosing the 50 students randomly. Smallįamilies who share the same surname cannot all be chosen, since this method will skip over students with the same surnames. This is called a systematic sample however, there is a small problem with bias with this sample method. One way we could choose the students would be to list them alphabetically by surname and then choose every fourth student. Let’s say we want to choose a sample of 50 students out of 200 students. There are many different ways we could choose a sample. We will focus on how we choose the members of the sample however, it is worth noting that the larger the sample size we choose, theīetter the assessment we get at the detriment of needing to collect more data. We want to choose a sample large enough to get a fairĪssessment from the population, and we also want to choose the sample in such a way as to minimize bias. Stem from the same source, which is the choice of the sample of the population. These two problems are very similar since bias in the sampling can cause the incorrect conclusion to be drawn. Therefore, the sample is more likely to have students who prefer soup While students who prefer salad will have no preference. In this case, the students who prefer soup may want to queue up earlier so they can get it while it is still hot, One way this could lead to bias is if soup is made at the start of the lunch break and gets colder over time, while the salad At first, it may seem like this is a fair way to choose the students, but this could lead toīias. An example for this could be by questioning theįirst 20 students in the lunch queue. Consider the lunch example if all 20 students weĪsked preferred soup, we may conclude that soup is the correct choice however, it could also be possible that we asked the onlyĪnother similar problem could be caused by bias in the choice of the sample group. However, sampling does have drawbacks.įor one, we may come to the wrong conclusion from only looking at the sample. For example, it is much easier to ask 20 students for their lunch Samples are a great way of obtaining information quickly. The entire set of objects we are analyzing is called the population.Ī smaller subset or selection of the population is called a sample of the population, and we call the size of this set the sample
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