Question

What are the common types of data distribution?

Answer 1

The common types of data distribution include:

1. Normal Distribution (Gaussian Distribution): It is a bell-shaped distribution where the data clusters around the mean with a symmetrical pattern. Examples of normally distributed data include heights, weights, and IQ scores.

2. Uniform Distribution: In a uniform distribution, the data points are evenly spread across the range without any particular pattern. A simple example of uniform distribution is rolling a fair die, where each outcome has an equal probability.

3. Skewed Distribution: Skewed distributions have an asymmetric pattern. There are two types of skewness:

   • Positive Skewness (Right Skew): The tail of the distribution is on the right side, and the majority of the data is concentrated on the left. An example is the distribution of income, where a few people have high incomes, resulting in a long tail on the right.
   • Negative Skewness (Left Skew): The tail of the distribution is on the left side, and the majority of the data is concentrated on the right. An example could be the distribution of age of death, where most people live to a certain age, resulting in a long tail on the left.

4. Exponential Distribution: Exponential distribution represents the time between events in a Poisson process, where events occur continuously and independently at a constant average rate. It is commonly used to model waiting times or lifetimes.

5. Bimodal Distribution: Bimodal distributions have two distinct peaks or modes, indicating the presence of two different groups or processes in the data. An example could be the distribution of exam scores, where one peak represents students who performed well and the other peak represents students who performed poorly.