When outliers are encountered in statistical analysis: >> First, each outlier is examined to understand its origin - is it a data entry error, a rare but valid observation, or a result of measurement variation? >> Depending on the context, robust statistical methods like median-based or trimmed analyses are used to minimize outlier's impact. >> If outliers represent meaningful data points, they are retained and addressed in interpretation. >> Documenting these decisions clearly allows transparent communication with stakeholders, ensuring the analysis remains accurate and credible without undue influence from outliers.

First of all, the question itself is biased, as it implies outliers are "bad". Outliers might reveal useful information, e.g. a problem in simulations, or some behavior that was not considered at the beginning of the investigation. Outliers should not be treated as a problem, but as "hints". They reveal that something requires extra attention. They should be discarded from a dataset only if, after looking further into them, the conclusion is that they were the result of a mistake.

Outliers are not bad always and we should be open to look at them for any insights they provide and value they bring to the analysis. What is the goal of your analysis will determine what role outliers play. There are proven methods to understand, categorize and deal with outliers in different scenarios. Dealing with them can be imputing, removal etc or in some cases using models such as decision trees. There is no one single way and it can be a combination of methods. The most important aspect is not discarding the outliers quickly.

If you’re training a predictive model, opt for tree based methods over linear. Consider transforming outliers using a technique like winsorizing. For identifying outliers, you can use anomaly detection methods. Anomaly detection can help especially to identify multivariate outliers, meaning outlier can a combined set of variables as frequently seen in highly correlated feature sets. If the outliers are few, consider dropping the observations from your training dataset.

Recommended SOPs are: Identify Outliers: Use visualization techniques (box plots, scatter plots) and statistical methods (Z-scores, IQR) to pinpoint outliers. Assess Impact: Determine if outliers are due to errors or genuine extreme values. If errors, correct or remove. If genuine, consider their impact on the analysis. Robust Methods: Employ statistical methods robust to outliers, like: -Robust Regression: Less sensitive to outliers. -Non-parametric Tests: Make fewer assumptions, reducing outlier influence. Trimmed Mean and Median: Less affected by extreme values. Transformations: Consider log or square root transformations to reduce outlier impact. Sensitivity Analysis: Run analysis with and without outliers to assess their influence.

We can identify outliers by using different techniques like Isolation Forest, Local Outlier Factor, IQR or Z-score. However, not all detected outliers are errors. In many cases, they represent valid data points, and that’s where caution comes in, especially with techniques like winsorization, which could mask meaningful extremes. In certain fields, outliers can carry unique significance. Take automotive data, for example—high-mileage electric vehicles might indicate fleet use rather than an error. This is where domain expertise becomes crucial. Balancing technical rigor with domain understanding is key to uncovering the full story behind the data.

Depending on the problem you are solving , it may or may not make sense to remove outliers. I think that if the data is real and outliers make business sense , you should keep them and use methods like log transformation to make data less noisy. If the outliers truly represent bad data (and don’t make business sense), then you can cap your data to get rid of them by using either a 1) 1.5 x IQR 2) mean +- 2 sigma 3) chose extreme percentiles 99% or above and 1% or below

From experience, I handle outliers by first visualizing the data with box plots or scatter plots to distinguish true anomalies. Then, I apply statistical methods like z-scores or IQR to identify them. Context is key—especially in fields like crop health, where outliers might indicate disease. Consulting domain experts helps decide whether to retain or exclude these points. When necessary, I use robust methods, like median-based measures, to minimize skew. Lastly, I conduct sensitivity tests with and without outliers to assess their impact, ensuring they don’t unduly influence my results.