# How to Identify an Outlier

An outlier is a value in a dataset that is significantly different from the other values in the dataset. In other words, an outlier is a data point that is very far away from the other data points.

For example, if a dataset consists of the numbers 2, 4, 5, 7, and 100, the number 100 would be considered an outlier because it is much larger than the other values in the dataset.

To find an outlier, you first need to determine the central tendency of the dataset, which can be done by calculating the mean, median, or mode.

Once you have identified the central tendency of the dataset, you can then look for data points that are significantly different from the other values.

## A step-by-step guide to finding an outlier

here’s a step-by-step guide to finding an outlier for grade 6 math:

1. First, determine the data set you want to analyze. This could be a set of test scores or grades for a particular class or group of students.
2. Next, arrange the data set in order from smallest to largest.
3. Calculate the median of the data set. The median is the middle number when the data set is arranged in order.
4. Calculate the interquartile range (IQR). The IQR is the difference between the first quartile (Q1) and the third quartile (Q3). To find Q1 and Q3, first find the median. Then find the median of the lower half of the data set to get Q1, and find the median of the upper half of the data set to get Q3.
5. Determine the lower and upper limits for outliers. To do this, multiply the IQR by 1.5 and add/subtract the result from Q1 and Q3, respectively. Any data point that falls outside these limits is considered an outlier.
6. Identify any data points that fall outside the lower and upper limits. These are the outliers in the data set.

Keep in mind that finding an outlier does not necessarily mean that the data point is incorrect or should be removed from the data set.

Outliers can occur due to a variety of reasons, including measurement error, natural variation, or unusual circumstances.

It’s important to consider the context of the data and use your judgment to determine if an outlier is valid or not.

### Finding an Outlier – Example 1

Choose the outlier in the data set.
18,17,21,24,17,15,20,23,18,4,19,21,24,19
Solution:
Identify a lot bigger or a lot littler values in a data set.
Since the value 4 is much smaller than all other values, it is an outlier.

### Finding an Outlier – Example 2

Choose the outlier in the data set.
120, 133, 124, 134, 121, 126, 135, 195, 120, 133, 121
Solution:
Identify a lot bigger or a lot littler values in a data set.
Since the value 195 is much bigger than all other values, it is an outlier.

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