What does a skewed distribution generally indicate about the data?

A skewed distribution provides valuable insights into the nature of the data set. When a distribution is skewed, it typically indicates that the data is not symmetrically distributed around the mean. Instead, the values are concentrated on one side of the distribution, which commonly signifies that there are outliers affecting the average.

In a skewed distribution, whether positively or negatively skewed, the presence of extreme values can pull the mean toward the tail of the distribution. This means that while most data points may cluster in a particular region, the outliers can significantly skew the mean, making it not truly representative of the central tendency of the data. Thus, recognizing skewness can alert analysts and researchers to the fact that the average value might be misleading due to these outliers.

Skewness contrasts sharply with a normal distribution, where data points cluster symmetrically around the mean. This is fundamental in understanding how outliers can distort interpretations of the data's central tendency, making acknowledging the skew an essential aspect of data analysis.