Which type of distribution represents data with extreme values on one side?

Which type of distribution represents data with extreme values on one side?

• A. Uniform distribution
• B. Normal distribution
• C. Skewed distribution
• D. Bimodal distribution

Answer: (C)

The type of distribution that represents data with extreme values on one side is skewed distribution. In statistical terms, skewness refers to the asymmetry of the data distribution. A distribution is considered skewed when it has a long tail on one side, indicating that there are more extreme values or outliers on that end. When the tail of the distribution is longer on the right side, it is called right-skewed or positively skewed; similarly, if the tail is longer on the left, it is referred to as left-skewed or negatively skewed. This characteristic of skewed distributions allows researchers to identify when data is not symmetrically distributed, which can be particularly important when analyzing trends or making predictions based on that data. In contrast, uniform distribution indicates that all outcomes are equally likely, normal distribution depicts a symmetrical bell curve without extreme values, and bimodal distribution has two different modes or peaks in the data set, rather than a single skewed tail.

The type of distribution that represents data with extreme values on one side is skewed distribution. In statistical terms, skewness refers to the asymmetry of the data distribution. A distribution is considered skewed when it has a long tail on one side, indicating that there are more extreme values or outliers on that end.

When the tail of the distribution is longer on the right side, it is called right-skewed or positively skewed; similarly, if the tail is longer on the left, it is referred to as left-skewed or negatively skewed. This characteristic of skewed distributions allows researchers to identify when data is not symmetrically distributed, which can be particularly important when analyzing trends or making predictions based on that data.

In contrast, uniform distribution indicates that all outcomes are equally likely, normal distribution depicts a symmetrical bell curve without extreme values, and bimodal distribution has two different modes or peaks in the data set, rather than a single skewed tail.