Final answer:
In geometry, skew or skewness refers to the lack of symmetry in a data distribution, with distributions being either left-skewed (negative skew) or right-skewed (positive skew), which affects the relationship between the mean, median, and mode.
Step-by-step explanation:
In geometry, a skew or skewness refers to the asymmetry of a data distribution in comparison to a normal distribution. When data is not symmetrical, the distribution can either be skewed to the right (positive skew) or to the left (negative skew). In a right-skewed distribution, the greater values are more spread out, which means the tail on the right side of the distribution is longer. In contrast, a left-skewed distribution will have the lower values more spread out, resulting in a longer tail on the left side.
Skewness is important when analyzing the relationship between the mean, median, and mode of a data set. For a left-skewed distribution, the mean is typically less than the median, which is less than the mode. Conversely, for a right-skewed distribution, the mode is often less than the median, which is less than the mean. Understanding the skewness of data has implications in statistical analyses, especially in terms of understanding probability distributions and when considering measures of central tendency and variability.