When the mean and median of a distribution are different, it suggests that the distribution is not symmetrical. This is often an indication of skewness in the distribution.

1. If the mean is greater than the median, the distribution is said to be positively skewed. This means that there are a number of data points with values greater than the mean, pulling the mean value higher.

2. If the mean is less than the median, the distribution is negatively skewed. This means that there are a number of data points with values less than the mean, pulling the mean value lower.

3. The greater the difference between the mean and median, the higher the skewness.

4. In a perfectly symmetrical distribution, the mean and median would be the same.

5. However, it's important to note that having a different mean and median is not enough to determine the shape of the distribution. Other factors such as kurtosis and the range of data should also be considered.

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When the mean and median of a distribution are different, it suggests that the distribution is not symmetrical. This is often an indication of skewness in the distribution.

1. If the mean is greater than the median, the distribution is said to be positively skewed. This means that there are a number of data points with values greater than the mean, pulling the mean value higher.

2. If the mean is less than the median, the distribution is negatively skewed. This means that there are a number of data points with values less than the mean, pulling the mean value lower.

3. The greater the difference between the mean and median, the higher the skewness.

4. In a perfectly symmetrical distribution, the mean and median would be the same.

5. However, it's important to note that having a different mean and median is not enough to determine the shape of the distribution. Other factors such as kurtosis and the range of data should also be considered.

Knowee AI · Accepted Answer

When the mean and median of a distribution are different, it suggests that the distribution is not symmetrical. This is often an indication of skewness in the distribution.

1. If the mean is greater than the median, the distribution is said to be positively skewed. This means that there are a number of data points with values greater than the mean, pulling the mean value higher.

2. If the mean is less than the median, the distribution is negatively skewed. This means that there are a number of data points with values less than the mean, pulling the mean value lower.

3. The greater the difference between the mean and median, the higher the skewness.

4. In a perfectly symmetrical distribution, the mean and median would be the same.

5. However, it's important to note that having a different mean and median is not enough to determine the shape of the distribution. Other factors such as kurtosis and the range of data should also be considered.

What can we say about the distribution when the mean and median are different?

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