A Pearson's coefficient of skewness for a data set is calculated as \( s k_{p}=-0.3 \). What does this indicate? There is a large negative skewness about the mean in the data values. There is a small positive skewness about the mean in the data values. There is a small negative skewness about the mean in the data values. There is a large positive skewness about the mean in the data values.

A Pearson's coefficient of skewness of \( sk_p = -0.3 \) indicates a small negative skewness about the mean in the data values. This means that the distribution of your data is slightly skewed to the left, which implies that the left tail of the distribution is longer or fatter than the right tail. In simpler terms, there are more values that are lower than the mean. In practical terms, understanding skewness is important for data visualization and interpretation. For instance, if you're analyzing income data with a negative skewness, it suggests a majority of individuals earn close to the higher end, with few earning significantly lower. This affects decisions in budgeting and financial planning!