The term 'outlier' is used in statistics to describe a value or point that is significantly different from other values in a data set. Here are the steps to understand it:

1. In a statistical analysis, we usually collect a set of data or observations. These could be anything from the heights of a group of people, the scores on a test, or the number of sales in a month.

2. Most of these values will be clustered around a central value, known as the mean or average. For example, if we were looking at heights, most people might be between 5 and 6 feet tall.

3. However, there might be some values that are significantly different from the others. For example, there might be one person who is 7 feet tall. This value is much higher than the others, and so it stands out.

4. This is what we call an 'outlier'. It's a value that lies outside the overall pattern of a distribution.

5. Outliers can have a significant impact on our analysis. For example, they can skew the mean or average, making it higher or lower than it would be if the outlier was not included.

6. Therefore, when conducting a statistical analysis, it's important to identify and consider the impact of any outliers.

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The term 'outlier' is used in statistics to describe a value or point that is significantly different from other values in a data set. Here are the steps to understand it:

1. In a statistical analysis, we usually collect a set of data or observations. These could be anything from the heights of a group of people, the scores on a test, or the number of sales in a month.

2. Most of these values will be clustered around a central value, known as the mean or average. For example, if we were looking at heights, most people might be between 5 and 6 feet tall.

3. However, there might be some values that are significantly different from the others. For example, there might be one person who is 7 feet tall. This value is much higher than the others, and so it stands out.

4. This is what we call an 'outlier'. It's a value that lies outside the overall pattern of a distribution.

5. Outliers can have a significant impact on our analysis. For example, they can skew the mean or average, making it higher or lower than it would be if the outlier was not included.

6. Therefore, when conducting a statistical analysis, it's important to identify and consider the impact of any outliers.

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The term 'outlier' is used in statistics to describe a value or point that is significantly different from other values in a data set. Here are the steps to understand it:

1. In a statistical analysis, we usually collect a set of data or observations. These could be anything from the heights of a group of people, the scores on a test, or the number of sales in a month.

2. Most of these values will be clustered around a central value, known as the mean or average. For example, if we were looking at heights, most people might be between 5 and 6 feet tall.

3. However, there might be some values that are significantly different from the others. For example, there might be one person who is 7 feet tall. This value is much higher than the others, and so it stands out.

4. This is what we call an 'outlier'. It's a value that lies outside the overall pattern of a distribution.

5. Outliers can have a significant impact on our analysis. For example, they can skew the mean or average, making it higher or lower than it would be if the outlier was not included.

6. Therefore, when conducting a statistical analysis, it's important to identify and consider the impact of any outliers.

What does the term 'outlier' mean?

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