Categories → Data , Variables
The data distribution describes the pattern or spread of values that a variable takes on in a dataset.
In statistics, the distribution of a variable or data refers to the pattern or shape of the spread of values that the variable takes on. Essentially, it describes how frequently different values of the variable occur in a dataset.
A distribution can be visualized using a histogram, which displays the frequency of values that fall within different ranges or "bins" of the variable. The shape of a distribution can be influenced by factors such as the sample size, the range of values, and the underlying population from which the data was drawn.
Some common types of distributions include:
Normal (or Gaussian) distribution: This is a bell-shaped distribution that is symmetric around the mean value. Many natural phenomena, such as heights and weights of people, follow a normal distribution.
Skewed distribution: A distribution is said to be skewed if it is not symmetric, and instead has a long tail on one side or the other. A distribution can be positively skewed (with a long tail on the right side) or negatively skewed (with a long tail on the left side).
Uniform distribution: In a uniform distribution, all values of the variable are equally likely to occur. This can be visualized as a flat line in a histogram.
Bimodal distribution: A bimodal distribution has two distinct peaks, indicating that the data can be divided into two groups that are each centered around different values of the variable. A multimodal distribution has any number of modes or peaks greater than 1.
There are many other named distributions not included above, such as the ones in this list. We have also only considered univariate distributions.
Understanding the distribution of a variable or data is important for making statistical inferences and drawing conclusions from the data. For example, if a variable follows a normal distribution, we can use tools like the standard deviation and confidence intervals to make predictions about future values or compare different groups of data. Various tools can help to assess whether data matches a particular type of distribution.