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In this step, we will see an example of an outlier using Python.

First, we will generate 2 datasets, with and without having outliers in them

`data_without_outlier = [1, 2, 3, 3, 4, 5, 4] data_with_outlier = [1, 2, 3, 3, 4, 4, 700]`

Now we will import the statistics module. This module provides functions for calculating mathematical statistics of numeric, real-valued data.

`import statistics`

Now we will create a function

`calculate_statistics`

that will print the`mean`

,`median`

, and`standard deviation`

for these 2 datasets`def <<your code goes here>>(data): print("Mean: ", statistics.mean(data)) print("Median: ", statistics.median(data)) print("Standard deviation: ", statistics.stdev(data))`

Finally, we will call the above functions with the 2 datasets that we created in the first step to print their respective

`mean`

,`median`

, and`standard deviation`

values in separate cells`calculate_statistics(data_without_outlier) calculate_statistics(data_with_outlier)`

As you can see from the above observations, the mean and standard deviation varies with the presence of an outlier. However, the median is relatively not affected by the presence of outliers. But this is a relatively smaller dataset, so it is easier to detect the outliers. However, that becomes a difficult task in case of a larger, real-life dataset.

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