True. The mean, often referred to as the average, is calculated by summing all the values in a dataset and dividing that sum by the number of values. This means that every number in the dataset contributes to the final calculation of the mean.
When an extreme value, known as an outlier, is present in the dataset, it can significantly skew the mean. For example, consider the following dataset of incomes: $30,000, $32,000, $31,000, $29,000, and $1,000,000. The mean income would be impacted dramatically by the $1,000,000 value, resulting in a mean that does not reflect the majority of the incomes in the dataset.
In contrast, the median and mode, which are other measures of central tendency, are less affected by extreme values. The median represents the middle value when the numbers are arranged in order and is more resistant to outliers, offering a more accurate representation of the central tendency in such cases.
Hence, when evaluating datasets with potential outliers, it is crucial to consider which measure of central tendency provides the most accurate reflection of the data’s overall behavior. In summary, the mean is indeed the measure of central tendency most likely to be influenced by extreme or atypical values, making it essential for analysts to be mindful of the data characteristics when interpreting results.