Two characteristics of the mean are:
- Sensitivity to Outliers: The mean is sensitive to outliers, which are extreme values that differ significantly from the other values in a dataset. Outliers can have a substantial impact on the mean because it takes into account the value of each observation. A single outlier can distort the mean and make it an unreliable representation of the dataset.
- Reflects the Sum of Values: The mean incorporates the sum of all values in the dataset. It considers both the magnitude and position of each value when calculating the average. As a result, the mean can provide a comprehensive representation of the overall data distribution.
Two characteristics of the mode are:
- Represents the Most Common Value: The mode represents the value or category that occurs with the highest frequency in a dataset. It identifies the most typical or frequently occurring observation. The mode is useful for identifying the predominant characteristic or behavior of a dataset.
- Applicable to Both Numerical and Categorical Data: Unlike the mean, which is primarily used for numerical data, the mode can be used for both numerical and categorical data. It can determine the most common value in a dataset regardless of whether it consists of numerical or categorical observations. This makes the mode a versatile measure of central tendency.