Based on my experience, when handling outliers in data analysis, context is everything. It's crucial to align with the end goal and leverage domain knowledge to understand why those outliers exist - are they errors, or do they reveal something valuable? Collaborate with stakeholders to decide whether to adjust, transform, or keep them. The goal is to maintain data integrity while extracting meaningful insights.

It is important to distinguish trends from noise and not to ignore noise from the analysis. There are indeed a lot of outliers in research. To deal with outliers, you need to create a matrix of meanings and weights of data units. By filtering these meanings, you can find hidden ideas or highlight underlying trends while filtering out noise. It is important to distinguish trends from noise and not to ignore noise from the analysis.

I encountered outliers while analyzing customer transaction data for a retail project, with high-value purchases skewing results. To address this, I first assessed the outliers' impact by comparing results with and without them. Realizing they significantly affected revenue averages, I investigated further and found these were legitimate bulk purchases, not errors. After consulting with the team, we decided to segment these outliers into a separate category, reflecting both typical and high-value customer behavior. This approach preserved data integrity and provided more accurate, nuanced insights.

To identify outliers in a normal distribution, the U+2SD method is a straightforward approach: 1. Find the Mean and Standard Deviation: Typically, 95% of data falls within Mean ± 2SD, so anything outside this range could be flagged as an outlier. 2. Flag Potential Outliers: If a data point is above Mean + 2SD or below Mean - 2SD, it’s worth investigating. 3. Assess the Impact: Run your analysis both with and without the outliers to see how much they influence the results. 4. Investigate Further: Look into whether the outliers are errors or important, rare occurrences. You can also use Z-scores or IQR to cross-check, especially if your data isn’t normally distributed.

It's essential to evaluate the purpose of the analysis and how the outliers affect your objectives. Sometimes outliers represent valuable insights, while other times they may distort the findings. Bringing in different perspectives and using statistical tools to justify decisions helps balance conflicting interpretations. One thing I’ve found helpful is applying multiple methods, such as conducting sensitivity analyses or comparing results with and without outliers, to provide a clearer picture. Presenting these comparisons to the team ensures that the decision is data-driven and grounded in the specific context of the project, reducing subjective disagreements.

In my experience, dealing with outliers requires balancing data integrity with accurate interpretation. I usually start by examining the impact on overall results and decide whether it’s worth keeping or excluding. Investigating the root cause helps determine if it's an error or something worth exploring further. Something I found useful is consulting with others in the field to get different perspectives, as outliers can sometimes reveal new insights. The key is not rushing to conclusions—outliers often need more context before acting on them.

Identify and Understand: Use statistical methods and visualizations to identify outliers and understand their potential causes. Assess Impact: Evaluate the impact of outliers on key metrics and conclusions. If the impact is minimal, they may be retained. Consider Context: Analyze outliers within the context of the data and domain knowledge. Are they plausible or indicative of errors? Explore Alternatives: Experiment with different analysis methods, such as robust statistics or non-parametric tests, that are less sensitive to outliers. Communicate Findings: Clearly communicate the presence and potential impact of outliers, along with the steps taken to address them.

When handling outliers in the data, we need to first understand the WHY. Why are we analysing the data? What is the context here? Do we require the outliers in the dataset? For example if we want to detect anomaly activities in the average air pollution of a room (for example gas leakage), we need the outliers. But for a general model to predict the ambient air quality, we don't. When showing statistical data and insights as well, we need to check whether our metrics are affected by outliers. Mean is affected by outliers, median isn't. Both type of metrics help in telling various facets of the story and these metrics should be used carefully and contextually when there is the chance of having outliers.