# How to deal when you have too many outliers?

I have attached the boxplot of a variable called Fare(of a journey). This is a continuous variable which has outliers. According to some articles on outliers, I learned that any data point that is above/below the whiskers is an outlier. I also learned that the whisker distance is calculated by 75th percentile + 1.5*(Inter-Quartile Range).

In the case that I have attached, you can see there are too many outliers(200/891 observations). If I replace all these points with missing values(can be imputed later), won't it produce bias? Few articles asked to consider 3*IQR instead of 1.5*IQR. Should I do that way? How to deal when you have too many outliers?

• I would be very careful with the direction of your thinking. While some of your observations may fit some algebraic definition of an outlier, it seems to me that those "outliers" are in reality part of your data. Recall that the algebraic definitions are sensitive to the approximate symmetry or skew of the observations. I would first ask why you think you should remove these values or consider them outliers Commented Dec 8, 2017 at 19:12
• Agreed, these are not what would typically be considered outliers - it's just that your data are not well-described by a normal distribution. How to deal with them depends on the analysis you are trying to do. If you describe that, we may be able to advise you further.
– mkt
Commented Dec 8, 2017 at 19:29
• I would strongly recommend against replacing "outliers" with missing values. If anything it is worth considering a variance-stabilising transformation first. That said, please describe what you want to do with this data. Commented Dec 8, 2017 at 22:40
• As I am a beginner, I am working on the Titanic data set provided in kaggle. I learned from an online course that for any data science problem we need to tackle the outlier and then tackle the missing values. In the training dataset, there are 2 numeric variables. One is Age with many missing values and the other is Fare variable(box plot is attached) with 2 missing values. This is my motivation to detect or remove outliers as I don't want any bias while substituting the mean for the missing values. Is my approach right? Is it right to take the above values as it is? Commented Dec 9, 2017 at 8:53
• The objective is to predict the missing values in Age variable and also the overall target variable(whether a passenger has survived or not). To predict them I think the variable Fare is crucial. Pls advise me on this. Commented Dec 9, 2017 at 8:55