How to Find Outliers Meaning, Formula & Examples

I’m passionate about statistics, machine learning, and data visualization and I created Statology to be a resource for both students and teachers alike. My goal with this site is to help you learn statistics through using simple terms, plenty of real-world examples, and helpful illustrations. One of the reasons we want to check for outliers is to confirm the quality of our data. One of the potential sources for outliers in our data are values that are not correct. There are different potential sources for these “incorrect values”. Two potential sources are missing data and errors in data entry or recording.

Four ways of calculating outliers

This method is helpful if you have a few values on the extreme ends of your dataset, but you aren’t sure whether any of them might count as outliers. An anomaly, on the other hand, refers to a pattern in the data that does not conform to expected behavior. Anomalies can be indicative of novel, rare, or unexpected events. In many cases, detecting anomalies is crucial for identifying issues such as fraud, network intrusions, or system failures. Suitable for datasets with large sample sizes and where the underlying distribution of the data can be reasonably approximated by a normal distribution.

A better solution would be to adjust your method of analysis and to think carefully about why the outlier exists. You might also choose to run your analysis with and without the outlier and present both sets of results for the sake of transparency. The intuition behind Z-score is to describe any data point by finding their relationship with the Standard Deviation and Mean of the group of data points. Z-score is finding the distribution of data where mean is 0 and standard deviation is 1 i.e. normal distribution.

What is an Outlier in Statistics? A Definition

Well, while calculating the Z-score we re-scale and center the data and look for data points which are too far from zero. These data points which are way too far from zero will be treated as the outliers. In most of the cases a threshold of 3 or -3 is used i.e if the Z-score value is greater than or less than 3 or -3 respectively, that data point will be identified as outliers. A scatter plot , is a type of plot or mathematical diagram using Cartesian coordinates to display values for typically two variables for a set of data. Outliers are found from z-score calculations by observing the data points that are too far from 0 (mean). In many cases, the “too far” threshold will be +3 to -3, where anything above +3 or below -3 respectively will be considered outliers.

If you’d like to learn more about what it’s like to work as a data analyst, check out our free, 5-day data analytics short course. These inconsistencies may lead to reduced statistical significance in an analysis. Isolation Forest—otherwise known as iForest—is another anomaly detection algorithm.

How to find an outlier in an even dataset

In data analytics, analysts create data visualizations to present data graphically in a meaningful and impactful way, in order to present their findings to relevant stakeholders. These visualizations can easily show trends, patterns, and outliers from a large set of data in the form of maps, graphs and charts. When going through the process of data analysis, outliers can cause anomalies in the results obtained. This means that they require some special attention and, in some cases, will need to be removed in order to analyze data effectively.

An outlier is a data point that significantly deviates from the rest of the data. It can be either much higher or much lower than the other data points, and its presence can have a significant impact on the results of machine learning algorithms. The analysis of outlier data is referred to as outlier analysis or outlier mining. Outliers can sometimes indicate errors or poor methods of sample gathering.

If you identify points that fall outside this range, these may be worth additional investigation. These extreme values can impact your statistical power as well, making it hard to detect a true effect if there is one. Outliers can have a big impact on your statistical analyses and skew the results of any hypothesis test if they are inaccurate.

• The intuition behind Z-score is to describe any data point by finding their relationship with the Standard Deviation and Mean of the group of data points.
• Since in k-means, you’ll be taking the mean a lot, you wind up with a lot of outlier-sensitive calculations.
• Outlier detection is a process of identifying observations or data points that significantly deviate from the majority of the data.
• To evaluate the strength of your findings, you’ll need to determine if the relationship between the two variables is statistically significant.

While what we do with outliers is defined by the specifics of the situation, by identifying them we give ourselves the tools to more confidently make decisions with our data. There are visualizations that can handle outliers more gracefully. One such method of visualizing the range of our data with outliers, is the box and whisker plot, or just “box plot”. The interquartile range (IQR) tells you the range of the middle half of your dataset.

Published in Towards Data Science

For example, say your data consists of the following values (15, 21, 25, 29, 32, 33, 40, 41, 49, 72). The Interquartile Range what is an outlier (IQR) is the distance between the first and third quartile. Subtract the first quartile from the third quartile to find the interquartile range. If you are interested in learning more about Statistics and the basics of Data Science, check out this free 8hour University course on freeCodeCamp’s YouTube channel.

It’s best to remove outliers only when you have a sound reason for doing so. For this reason, you should only remove outliers if you have legitimate reasons for doing so. It’s important to document each outlier you remove and your reasons so that other researchers can follow your procedures. You can sort quantitative variables from low to high and scan for extremely low or extremely high values. This type of outlier is problematic because it’s inaccurate and can distort your research results.

In data analytics, outliers are values within a dataset that vary greatly from the others—they’re either much larger, or significantly smaller. Outliers may indicate variabilities in a measurement, experimental errors, or a novelty. By now, it should be clear that finding outliers is an important step when analyzing our data! It helps us detect errors, allows us to separate anomalies from the overall trends, and can help us focus our attention on exceptions.

True outliers are also present in variables with skewed distributions where many data points are spread far from the mean in one direction. It’s important to select appropriate statistical tests or measures when you have a skewed distribution or many outliers. The data below shows the number of daily visitors to a museum. The data below shows a high school basketball player’s points per game in 10 consecutive games. Use the outlier formula and the given data to identify potential outliers. This means that a data point needs to fall more than 1.5 times the Interquartile range below the first quartile to be considered a low outlier.