Outlier Data

Outlier Data. 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 In a realworld example the average height of a giraffe is about 16 feet tall.

An outlier is defined as any observation in a dataset that is 15 IQRs greater than the third quartile or 15 IQRs less than the first quartile where IQR stands for “interquartile range” and is the difference between the first and third quartile To identify outliers for a given dataset enter your comma separated data in the box below then click the “Identify Outliers” button.

What are outliers and how to treat them in Data Analytics

Outliers are extreme values that differ from most other data points in a dataset They can have a big impact on your statistical analyses and skew the results of any hypothesis tests It’s important to carefully identify potential outliers in your dataset and deal with them in an appropriate manner for accurate results.

Outlier Wikipedia

In simple terms an outlier is an extremely high or extremely low data point relative to the nearest data point and the rest of the neighboring coexisting values in a data graph or dataset you’re working with Outliers are extreme values that stand out greatly from the overall pattern of values in a dataset or graph.

Data Analytics Explained: What Is an Outlier?

The difference is that outlier identification becomes a tool to help analyze the data perhaps by suggesting ways to reexpress or smooth them Key elements of the underlying conception are (1) that “outliers” often occur (2) they do not necessarily reflect “rogue” or “bad” data and (3) are treated as adding useful detail to the gross descriptions afforded by summary.