Question: An important practice is to check the validity of any data set that you analyze. One goal is to detect typos in the data, and another would be to detect faulty measurements. Recall that outliers are observations with values outside the “normal” range of values of the rest of the observations.
- 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.
Answer: I am interested in studying a large population, specifically students in a school, focusing on collecting numeric measurements such as the heights of the students. When working with a randomly selected sample of 100 students from the school, encountering outliers is not uncommon.
In such cases, the best statistical practice is to identify and remove outliers from the sample before analyzing the data further. This involves determining not only the lower quartile (Q1) and upper quartile (Q3) but also calculating additional measures such as the median, mean, and standard deviation to gain a comprehensive understanding of the data distribution.
The best course of action statistically, if i found 2 outliers in the sample is to remove the outliers and then find the required statistics. I will determine the lower quartile Q1 and the upper quartile Q3, then calculate the interquartile Range IQR which is given mathematically as Q3-Q1, determine the lower outlier given mathematically as Q1 – IQR1.5, and then find the upper outlier given as Q3 + IQR1.5.
Height of students outside of this range are considered as outliers and should be removed from the data in other for us to get better result. By employing these statistical techniques, we can obtain more meaningful insights and draw valid conclusions about the heights of the student population under study. Additionally, when encountering multiple outliers, such as two values, that significantly surpass the next highest value under the outlier criteria, it is advisable to omit those values from the analysis to prevent distortion of results and ensure the integrity of the dataset.