Identifying Outliers in a Large Population: A Statistical Analysis When studying a large population, data quality is paramount. Outliers are data points that are very different from the others. They can change the results and make them incorrect. We will talk about finding outliers in a group of "adult internet users from all over the world." The numeric measurement of interest is monthly average internet usage time (in hours). With a sample size of 100, finding few outliers is statistically expected. Outliers often become more apparent in larger datasets. However, even in a sample of 100, it's crucial to analyse potential outliers for data integrity. Identifying Potential Outliers: Boxplots: A boxplot visually depicts the distribution of data. Outliers are usually data points that are outside the whiskers on a graph. These whiskers show the highest and lowest points of the data, but not the outliers ([Infostat, 2023]). Interquartile Range (IQR): is the space between the 75th percentile (Q3) and the 25th percentile (Q1) in the data. Any numbers that are smaller than Q1 minus 1.5 times the IQR or larger than Q3 plus 1.5 times the IQR are considered potential outliers ([Field, 2016]). What comment would be suitable for this entry