What is the difference between skewed and uniform distribution?

Data Science, AI and ML
#1

A skewed distribution is neither symmetric nor normal because the data values trail off more sharply on one side than on the other. In business, you often find skewness in data sets that represent sizes using positive numbers (eg, sales or assets). The reason is that data values cannot be less than zero (imposing a boundary on one side) but are not restricted by a definite upper boundary. The result is that there are many data values concentrated near zero, and they become systematically fewer and fewer as you move to the right in the histogram.

In statistics, uniform distribution refers to a type of probability distribution in which all outcomes are equally likely. A deck of cards has within it uniform distributions because the likelihood of drawing a heart, a club, a diamond, or a spade is equally likely. A coin also has a uniform distribution because the probability of getting either heads or tails in a coin toss is the same.

The uniform distribution can be visualized as a straight horizontal line, so for a coin flip returning a head or tail, both have a probability p = 0.50 and would be depicted by a line from the y-axis at 0.50