What is a characteristic of negative skewed distributions?

A negative skewed distribution is characterized by a concentration of values on the low end, while the tail extends to the left. This means that most of the data points are clustered around higher values, and the few lower values pull the mean down, resulting in a tail that skews toward the lower end of the scale.

When analyzing this type of distribution, it is often observed that the mean is less than the median due to the influence of the lower values. This characteristic helps in understanding trends in data, as it indicates that while most observations are higher, there are some significant low values that affect the overall mean. This understanding is crucial in fields such as psychology, where data interpretation can guide research and conclusions based on participant responses or behaviors.

The other options do not accurately describe this distribution. For instance, evenly spread values would indicate a normal distribution, while identical mean and median values suggest a symmetrical distribution. A concentration of values on the high end describes a positive skew, which is opposite to what is observed in a negative skewed distribution.