What Is Outlier?

An "outlier" is an observation that deviates from the norm in a dataset. It's like going to a party when everyone else is dressed casually, but one person is wearing a tuxedo. They would stand out as an extreme case. As they cause the mean and other statistical measures to be skewed, outliers can greatly affect the analysis findings in statistics. If you had a dataset of people's heights in a given city, for instance, and one person was noticeably taller or shorter than the rest, that individual's height would be an outlier and have a large impact on the group's mean height. Many factors can contribute to an outlier's appearance. They could be due to data variations or mistakes made during data collection or processing. An outlier is an actual difference in the data; for instance, if you have a dataset of people's ages in a city and one person's age is much older or younger than everyone else, that person's age might be an outlier. Since outliers can distort the outcomes of analysis, they should be avoided whenever possible. It is common practice to handle this issue by first identifying and excluding outliers from the dataset. Statistical tools like the box plot method and the data-eye analysis can be used to find outliers. As soon as outliers are found, a decision must be made regarding whether or not to include them in the dataset. Outliers should be considered case-by-case; in certain circumstances, they may represent meaningful variances in the data and should be retained. In other cases, it might be better to get rid of them because mistakes in data collection or processing might cause them. That is a summary of extreme cases. Outliers are individual data points that differ greatly from the rest of a dataset and have the potential to skew the conclusions of a study. There are several methods for finding outliers, and it's crucial to make a call on whether or not to include them in the dataset.