Real-world datasets are messy. Outliers can hide patterns, distort models, and lead to bad decisions. In this article, we’ll walk through practical ways to detect them in Python - using plots, statistics, and machine learning, then apply it all on a real dataset.
So let’s start with the basics.
What Are Outliers#
Outliers are data points that significantly differ from the majority of observations in a dataset. In statistical terms, they are values that are far from the mean or the expected range. So, basically outliers are the extreme values in a dataset.
What are the reasons for outliers?#
So, why do outliers occur? in real-world datasets, outliers can occur for many reasons are broadly classified into 3 types:
- Data Entry Errors: Mistakes made by users or data collectors. For example, a data entry operator may have entered a wrong value for a data point. This is a common reason for outliers in real-world datasets.
- Instrument Errors: Occurs when the instrument used to collect data is not accurate. For example, a scale may be miscalibrated, or a thermometer may be faulty.
- Natural Variation: Occurs in naturally occurring data. For example, the height of a person in a population is normally distributed, but there are few people who are either very tall or very short. Those are very rare cases, but they are outliers.
What are the types of outliers?#
To effectively handle outliers in your data, you first need to understand how they behave. Different outliers present themselves in different ways, and recognizing these patterns helps you choose the right detection method. Let’s break down the main categories:
- Univariate Outliers: These are the simplest to spot - they’re extreme values within a single column of data. For instance, if most exam scores fall between 65-85 points, but one student has 15 points, that’s clearly standing out from the rest.
- Multivariate Outliers: Here’s where things get trickier. A data point might look normal when you check each variable separately, but becomes an outlier when you examine variables together. Picture a real estate listing: a $650,000 price tag might seem reasonable for luxury homes, and 1,400 sq ft is fine for smaller properties - but combine them? That price for that size doesn’t match the market pattern where similar square footage sells for $200,000-$300,000.
- Contextual Outliers: The same value can be normal or unusual depending on when or where it occurs. A temperature reading of 95°F is expected during summer months but would be highly unusual in the middle of winter - the value itself isn’t inherently an outlier, the context determines it.
- Collective Outliers: Instead of individual points, you might find groups of observations that collectively deviate from normal behavior. Consider an online store averaging 150 daily orders suddenly jumping to 900 orders for a week straight outside any sale period - the entire sequence breaks the expected pattern.
Recognizing these different outlier types is just the starting point. What really matters is understanding how they can distort your data analysis and machine learning models, potentially leading to flawed insights and poor decisions.
Why do we care about outliers?#
When outliers sneak into your dataset, they don’t just sit quietly - they actively mess with your results. Here’s what happens when you ignore them:
Statistical Measures Get Distorted: Think about calculating average income in a neighborhood. If most people earn $50,000-$80,000 annually, but one resident is a billionaire, that average becomes meaningless. Outliers pull measures like mean and standard deviation away from what’s truly representative of your data.
Model Assumptions Break Down: Many statistical techniques - linear regression, t-tests, ANOVA - assume your data follows a nice, bell-shaped curve. Outliers stretch and skew this distribution, violating these fundamental assumptions and making your test results unreliable.
Machine Learning Models Suffer: When training predictive models, outliers act like loud voices in a conversation - they demand attention. The model might spend too much effort trying to fit these extreme cases, learning patterns that don’t generalize well to new, unseen data.
Data Quality Red Flags: Sometimes outliers aren’t just statistical nuisances - they’re telling you something’s wrong. Maybe someone typed 1500 instead of 150, or a sensor malfunctioned. Spotting outliers can help you catch these data collection and entry problems before they poison your entire analysis.
How do we detect outliers?#
There are many ways to detect outliers in a dataset. Here are some of the most common methods: