Specify a large population that you might want to study and describe the type of numeric measurement that you will collect (examples: a count of things, the height of people, a score on a survey, the weight of something) for your study. What is the best course of action statistically if you found few outliers in a sample of size 100?
To answer the above questions:
- Outline the method(s) you will use if two values twice as big as the next highest value were identified in the sample.
- You may use examples from your area of interest, such as monthly sales levels of a product, file transfer times to different computer on a network, characteristics of people (height, time to run the 100-meter dash, statistics grades, etc.), trading volume on a stock exchange, or other such things.
For the purpose of this discussion, let us consider a large population of people and the numeric measurement we will collect is their eyesight. Specifically, we will measure their visual acuity using the Snellen chart, which is a standard chart used by optometrists and ophthalmologists to measure visual acuity (Boslaugh, n.d.).
If we found few outliers in a sample of size 100, the best course of action statistically would be to investigate the outliers to determine if they are valid or not. If they are valid, we should include them in our analysis as they may provide valuable insights into the population. However, if they are not valid, we should remove them from our analysis to prevent them from skewing our results.
For example, let us say we collected visual acuity data from a sample of 100 people and found that two people had visual acuity values that were twice as big as the next highest value. In this case, we would investigate these outliers to determine if they are valid or not. We may find that these two people have an eye condition that affects their visual acuity or that they did not follow the instructions properly during the test. If we determine that these outliers are valid, we should include them in our analysis as they may provide insights into the population. However, if we determine that these outliers are not valid, we should remove them from our analysis to prevent them from skewing our results.
In general, when dealing with outliers, it is important to investigate them thoroughly to determine if they are valid or not. If they are valid, they should be included in the analysis as they may provide valuable insights into the population. However, if they are not valid, they should be removed from the analysis to prevent them from skewing the results (Glen, n.d.). It is also important to note that outliers can occur due to various reasons such as measurement errors, data entry errors, or genuine extreme values in the population. Therefore, it is essential to have a good understanding of the data and the population being studied to make informed decisions about how to handle outliers.
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References
Boslaugh, S. (n.d.). Snellen chart. Retrieved from https://www.britannica.com/science/Snellen-chart Glen, S. (n.d.) Outliers: Finding Them in Data, Formula, Examples. Easy Steps and Video From StatisticsHowTo.com: Elementary Statistics for the rest of us! https://www.statisticshowto.com/statistics-basics/find-outliers/