{"id":9769,"date":"2021-09-23T12:12:54","date_gmt":"2021-09-23T10:12:54","guid":{"rendered":"https:\/\/careerfoundry.inbearbeitung.de\/en\/?p=9769"},"modified":"2023-09-28T12:13:45","modified_gmt":"2023-09-28T10:13:45","slug":"what-is-an-outlier","status":"publish","type":"post","link":"https:\/\/careerfoundry.inbearbeitung.de\/en\/blog\/data-analytics\/what-is-an-outlier\/","title":{"rendered":"What Is an Outlier?"},"content":{"rendered":"

When it comes to working in data analytics<\/a>\u2014whether that\u2019s as a data analyst or in a role that involves data in another capacity\u2014there’s a long process involved, way before the actual analysis phase begins.<\/strong> <\/span><\/p>\n

In fact, up to two-thirds of the time taken in the data analytics process is spent cleaning what\u2019s known as \u201cdirty\u201d data: data that needs to be edited, worked on, or otherwise manipulated before it\u2019s suitable for analysis.\u00a0<\/span><\/p>\n

During the cleaning phase, a data analyst may find outliers in the \u201cdirty\u201d data, which leads to either removing them from the dataset entirely, or handling them in another way. And so begs the question: what is an outlier?<\/span><\/p>\n

If you’re interested, why not try CareerFoundry’s free, 5-day data analytics course<\/a><\/strong>?\u00a0Otherwise, t<\/span>o skip ahead, just use the clickable menu:<\/span><\/p>\n

    \n
  1. What is an outlier?<\/a><\/strong><\/li>\n
  2. How do outliers end up in datasets?<\/a><\/strong><\/li>\n
  3. How can you identify outliers?<\/a><\/strong>\n
      \n
    1. Using visualizations<\/a><\/strong><\/li>\n
    2. Using statistical methods<\/a><\/strong><\/li>\n<\/ol>\n<\/li>\n
    3. When should you remove outliers?<\/a><\/strong><\/li>\n
    4. Wrap-up and next steps<\/a><\/strong><\/li>\n<\/ol>\n

      With that, let\u2019s begin.<\/span><\/p>\n

      1. What is an outlier?<\/span><\/h2>\n

      In data analytics, outliers are values within a dataset that vary greatly from the others\u2014they\u2019re either much larger, or significantly smaller. Outliers may indicate variabilities in a measurement, experimental errors, or a novelty. <\/span><\/p>\n

      In a real-world example, the average height of a giraffe is about 16 feet tall. However, there have been recent discoveries of two giraffes that stand at 9 feet and 8.5 feet, respectively. These two giraffes would be considered outliers in comparison to the general giraffe population.\u00a0<\/span><\/p>\n

      When going through <\/span>the process of data analysis<\/span><\/a>, 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.<\/span><\/p>\n

      There are two main reasons why giving outliers special attention is a necessary aspect of the data analytics process:<\/span>
      \n<\/span><\/p>\n

        \n
      1. Outliers may have a negative effect on the result of an analysis<\/span><\/li>\n
      2. Outliers\u2014or their behavior\u2014may be the information that a data analyst requires from the analysis<\/span><\/li>\n<\/ol>\n

        Types of outliers<\/span><\/h3>\n

        There are two kinds of outliers:<\/span>
        \n<\/span><\/p>\n