Skewed Distribution:
A skewed distribution, also known as a non-symmetrical distribution, is a statistical term that refers to the shape of a probability distribution. In a skewed distribution, the data is not evenly distributed around the mean but is instead concentrated towards one tail of the distribution.
Types of Skewed Distribution:
Skewed distributions are further classified into two main types:
- Positive Skew: When the tail of the distribution extends towards the right, it is referred to as a positive skew. In a positively skewed distribution, the majority of the data is concentrated towards the lower values, while a few extremely high values pull the mean in the positive direction.
- Negative Skew: Conversely, when the tail of the distribution extends towards the left, it is called a negative skew. In a negatively skewed distribution, the majority of the data is concentrated towards the higher values, while a few extremely low values pull the mean in the negative direction.
Characteristics of Skewed Distribution:
A skewed distribution exhibits the following characteristics:
- The mean, median, and mode of the distribution are unequal.
- The tail of the distribution is longer on the side towards which it is skewed, making it asymmetrical.
- The majority of the data points cluster towards one side of the distribution.
- Outliers or extreme values can significantly impact the mean, leading to its deviation from the median.
Significance of Skewed Distribution:
Understanding skewed distributions is crucial for accurate data analysis and interpretation. Skewness provides insights into the shape and distribution of data, helping in identifying potential outliers, predicting trends, and making appropriate statistical inferences.